Статті в журналах з теми "Low-light enhancement and denoising"
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1
Carré, Maxime, and Michel Jourlin. "Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising." Sensors 21, no. 23 (November 27, 2021): 7906. http://dx.doi.org/10.3390/s21237906.
Повний текст джерелаАнотація:
Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.
2
Zhang, Jialiang, Ruiwen Ji, Jingwen Wang, Hongcheng Sun, and Mingye Ju. "DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising." Electronics 12, no. 14 (July 11, 2023): 3038. http://dx.doi.org/10.3390/electronics12143038.
Повний текст джерелаАнотація:
Images taken in low-light situations frequently have a significant quality reduction. Taking care of these degradation problems in low-light images is essential for raising their visual quality and enhancing high-level visual task performance. However, because of the inherent information loss in dark images, conventional Retinex-based approaches for low-light image enhancement frequently fail to accomplish real denoising. This research introduces DEGANet, a revolutionary deep-learning framework created particularly for improving and denoising low-light images. To overcome these restrictions, DEGANet makes use of the strength of a Generative Adversarial Network (GAN). The Decom-Net, Enhance-Net, and an Adversarial Generative Network (GAN) are three linked subnets that make up our novel Retinex-based DEGANet architecture. The Decom-Net is in charge of separating the reflectance and illumination components from the input low-light image. This decomposition enables Enhance-Net to effectively enhance the illumination component, thereby improving the overall image quality. Due to the complicated noise patterns, fluctuating intensities, and intrinsic information loss in low-light images, denoising them presents a significant challenge. By incorporating a GAN into our architecture, DEGANet is able to effectively denoise and smooth the enhanced image as well as retrieve the original data and fill in the gaps, producing an output that is aesthetically beautiful while maintaining key features. Through a comprehensive set of studies, we demonstrate that DEGANet exceeds current state-of-the-art methods in both terms of image enhancement and denoising quality.
3
Kim, Minjae, Dubok Park, David Han, and Hanseok Ko. "A novel approach for denoising and enhancement of extremely low-light video." IEEE Transactions on Consumer Electronics 61, no. 1 (February 2015): 72–80. http://dx.doi.org/10.1109/tce.2015.7064113.
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Malik, Sameer, and Rajiv Soundararajan. "A low light natural image statistical model for joint contrast enhancement and denoising." Signal Processing: Image Communication 99 (November 2021): 116433. http://dx.doi.org/10.1016/j.image.2021.116433.
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Das Mou, Trisha, Saadia Binte Alam, Md Hasibur Rahman, Gautam Srivastava, Mahady Hasan, and Mohammad Faisal Uddin. "Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation." Applied Sciences 13, no. 2 (January 12, 2023): 1034. http://dx.doi.org/10.3390/app13021034.
Повний текст джерелаАнотація:
Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various Gaussian convolution kernels. In addition, backpropagation optimizes most of the parameters in this model, whereas the values in conventional models depend on an artificial environment. The model was constructed using simultaneous multi-resolution convolution and dual attention processes. We performed our experiment in the Tesla T4 GPU of Google Colab using the Customized Raw Image Dataset, College Image Dataset (CID), Extreme low-light denoising dataset (ELD), and ExDark dataset. In this approach, an extended set of features is set up to learn from several scales to incorporate contextual data. An extensive performance evaluation on the four above-mentioned standard image datasets showed that MSR-MIRNeT produced standard image enhancement and denoising results with a precision of 97.33%; additionally, the PSNR/SSIM result is 29.73/0.963 which is better than previously established models (MSR, MIRNet, etc.). Furthermore, the output of the proposed model (MSR-MIRNet) shows that this model can be implemented in medical image processing, such as detecting fine scars on pelvic bone segmentation imaging, enhancing contrast for tuberculosis analysis, and being beneficial for robotic visualization in dark environments.
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Han, Guang, Yingfan Wang, Jixin Liu, and Fanyu Zeng. "Low-light images enhancement and denoising network based on unsupervised learning multi-stream feature modeling." Journal of Visual Communication and Image Representation 96 (October 2023): 103932. http://dx.doi.org/10.1016/j.jvcir.2023.103932.
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Hu, Linshu, Mengjiao Qin, Feng Zhang, Zhenhong Du, and Renyi Liu. "RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images." Remote Sensing 13, no. 1 (December 26, 2020): 62. http://dx.doi.org/10.3390/rs13010062.
Повний текст джерелаАнотація:
Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures.
8
Zhao, Meng Ling, and Min Xia Jiang. "Research on Enhanced of Mine-Underground Picture." Advanced Materials Research 490-495 (March 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.548.
Повний текст джерелаАнотація:
Because of the based on S3C6410 Field information recorder mine- underground non-uniform illumination and mine- underground non-uniform illumination that a large of noise collected and transferred,image is low contrast ,dim and dark. Based on the theory of Donoho's wavelet threshold denoising, several typical wavelet threshold denoising methods are compared.the best denoising effect of peak signal to noise ratio is obtained. The image enhancement method that combination of the adaptive thresholding denoising and histogram equalization is proposed. The experiment result shows that the method has a good denoising performance, which removed the readout noise of CCD Camera,at the same time, image quality is improved .So the wavelet enhancement in image processing of mine- underground can improve image quality.
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Wu, Zeju, Yang Ji, Lijun Song, and Jianyuan Sun. "Underwater Image Enhancement Based on Color Correction and Detail Enhancement." Journal of Marine Science and Engineering 10, no. 10 (October 17, 2022): 1513. http://dx.doi.org/10.3390/jmse10101513.
Повний текст джерелаАнотація:
To solve the problems of underwater image color deviation, low contrast, and blurred details, an algorithm based on color correction and detail enhancement is proposed. First, the improved nonlocal means denoising algorithm is used to denoise the underwater image. The combination of Gaussian weighted spatial distance and Gaussian weighted Euclidean distance is used as the index of nonlocal means denoising algorithm to measure the similarity of structural blocks. The improved algorithm can retain more edge features and texture information while maintaining noise reduction ability. Then, the improved U-Net is used for color correction. Introducing residual structure and attention mechanism into U-Net can effectively enhance feature extraction ability and prevent network degradation. Finally, a sharpening algorithm based on maximum a posteriori is proposed to enhance the image after color correction, which can increase the detailed information of the image without expanding the noise. The experimental results show that the proposed algorithm has a remarkable effect on underwater image enhancement.
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Xu, Xiaogang, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. "Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 3054–62. http://dx.doi.org/10.1609/aaai.v37i3.25409.
Повний текст джерелаАнотація:
Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. Also, a dynamic residual frame is jointly learned with the DP3DF via a shared backbone to further boost the SR quality. We performed extensive experiments, including a large-scale user study, to show our method's effectiveness. Our method consistently surpasses the best state-of-the-art methods on all the challenging real datasets with top PSNR and user ratings, yet having a very fast run time. The code is available at https://github.com/xiaogang00/DP3DF.
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Tian, Mingchuan, and Jizheng Liu. "Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising." Wireless Communications and Mobile Computing 2022 (July 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5167639.
Повний текст джерелаАнотація:
In order to improve the transmission effect of low-power communication signal of Internet of Things and compress the enhancement time of low-power communication signal, this paper designs a low-power communication signal enhancement method of Internet of Things based on nonlocal mean denoising. Firstly, the residual of one-dimensional communication layer is preprocessed by convolution core to obtain the residual of one-dimensional communication layer. Then, according to the two classification recognition methods, the noise reduction signal feature recognition of the low-power communication signal of the Internet of Things is realized, the nonlocal mean noise reduction algorithm is used to remove the low-power communication signal of the Internet of Things, and the weight value between similar blocks is calculated according to the European distance method. Finally, the low-power communication signal enhancement of the Internet of Things is realized by the nonlocal mean value denoising method. The experimental results show that the communication signal enhancement time overhead of this method is low, which is always less than 2.6 s. The lowest bit error rate after signal enhancement is about 1%, and the signal-to-noise ratio is up to 18 dB, which shows that this method can achieve signal enhancement.
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Hassan, Raaid N. "A comparison between PCA and some enhancement filters for denoising astronomical images." Iraqi Journal of Physics (IJP) 11, no. 22 (February 20, 2019): 82–92. http://dx.doi.org/10.30723/ijp.v11i22.356.
Повний текст джерелаАнотація:
This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around the corresponding pixel in the input image, Gaussian low pass-filter and Order-statistic filter also be used.
Experimental results shows LPG-PCA method gives better performance, especially in image fine structure preservation, compared with other general denoising algorithms.
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Sun, Qingjiao, Huiyan Jiang, Ganzheng Zhu, Siqi Li, Shang Gong, Benqiang Yang, and Libo Zhang. "HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7478219.
Повний текст джерелаАнотація:
Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.
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Yu, Lijia, Jie Luo, Shaoping Xu, Xiaojun Chen, and Nan Xiao. "An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers." Applied Sciences 12, no. 12 (June 19, 2022): 6227. http://dx.doi.org/10.3390/app12126227.
Повний текст джерелаАнотація:
Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level image combination strategy to leverage their respective advantages for the denoised images (referred to as initial denoised images) generated by individual denoisers. The key to this combination strategy is to generate a corresponding weight map of the same size for each initial denoised image. To this end, we introduce an unsupervised weight map generative network that adjusts its parameters to generate a weight map for each initial denoised image under the guidance of our designed loss function. Using the weight maps, we are able to fully utilize the internal and external information of various denoising methods at a finer granularity, ensuring that the final combined image is close to the optimal. To the best of our knowledge, our enhancement method of combining denoised images at the pixel level is the first proposed in the image combination field. Extensive experiments demonstrate that the proposed method shows superior performance, both quantitatively and visually, and stronger generalization. Specifically, in comparison with the stand-alone denoising methods FFDNet and BM3D, our method improves the average peak signal-to-noise ratio (PSNR) by 0.18 dB to 0.83 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone methods and combination methods, and has surpassed the denoising effect of the second-best method by 0.03 dB to 1.42 dB. It should be noted that since our image combination strategy is generic, the proposed combined strategy can not only be used for image denoising but can also be extended to low-light image enhancement, image deblurring or image super-resolution.
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Li, Shunlei, Muhammad Adeel Azam, Ajay Gunalan, and Leonardo S. Mattos. "One-Step Enhancer: Deblurring and Denoising of OCT Images." Applied Sciences 12, no. 19 (October 7, 2022): 10092. http://dx.doi.org/10.3390/app121910092.
Повний текст джерелаАнотація:
Optical coherence tomography (OCT) is a rapidly evolving imaging technology that combines a broadband and low-coherence light source with interferometry and signal processing to produce high-resolution images of living tissues. However, the speckle noise introduced by the low-coherence interferometry and the blur from device motions significantly degrade the quality of OCT images. Convolutional neural networks (CNNs) are a potential solution to deal with these issues and enhance OCT image quality. However, training such networks based on traditional supervised learning methods is impractical due to the lack of clean ground truth images. Consequently, this research proposes an unsupervised learning method for OCT image enhancement, termed one-step enhancer (OSE). Specifically, OSE performs denoising and deblurring based on a single step process. A generative adversarial network (GAN) is used for this. Encoders disentangle the raw images into a content domain, blur domain and noise domain to extract features. Then, the generator can generate clean images from the extracted features. To regularize the distribution range of retrieved blur characteristics, KL divergence loss is employed. Meanwhile, noise patches are enforced to promote more accurate disentanglement. These strategies considerably increase the effectiveness of GAN training for OCT image enhancement when used jointly. Both quantitative and qualitative visual findings demonstrate that the proposed method is effective for OCT image denoising and deblurring. These results are significant not only to provide an enhanced visual experience for clinicians but also to supply good quality data for OCT-guide operations. The enhanced images are needed, e.g., for the development of robust, reliable and accurate autonomous OCT-guided surgical robotic systems.
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Jiang, Hui Qin, Zhong Yong Wang, Ling Ma, Yu Min Liu, and Ping Li. "Wavelet-Based Medical Image Denoising and Enhancement." Applied Mechanics and Materials 195-196 (August 2012): 515–20. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.515.
Повний текст джерелаАнотація:
The visual quality of medical images is an important aspect in PACS implementation. In this study, on the basis of wavelet analysis, a denoising and enhancement algorithm for medical image is proposed. The algorithm mainly includes six steps. At first, an effcient method is investigated for Poisson Noise remove. Second, diagnosis features of the denoised image are enhanced by compressing the dynamic range. Third, we extract the high frequency component of the original image by the designed lowpass filter. Fourth, the extracted high frequency component are segment into diagnosis feature component in the high signal range, the diagnosis feature component in the low signal range, and the noise component. Five, we reconstruct an image using image fusion. Finally, we make DICOM calibration for used display and decide parameters of the image fusion, resulting in the diagnosis image. Experimental results show that this new scheme offers effective noise removal in medical images and enhancing sharpness. More importantly, this scheme can improve the diagnostic value of the display image on the commercial display successfully.
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Cutmore, Tim R. H., and Patrick Celka. "Composite Noise Reduction of ERPs Using Wavelet, Model-Based, and Principal Component Subspace Methods." Journal of Psychophysiology 22, no. 3 (January 2008): 111–20. http://dx.doi.org/10.1027/0269-8803.22.3.111.
Повний текст джерелаАнотація:
This paper used three theoretically different algorithms for reducing noise in event-related potential (ERP) data. It examined the possibility that a hybrid of these methods could show gains in noise reduction beyond that obtained with any single method. The well-known ERP oddball paradigm was used to evaluate three denoising methods: statistical wavelet transform (wavelet-Z), a smooth subspace wavelet filter (wavelet-S), and subspace PCA. The six possible orders of serial application of these methods to the oddball waveforms were compared for efficacy in signal enhancement. It was found that the order was not commutative, with the best results obtained from applying the wavelet-Z first. Comparison of oddball and frequent trials in the grand average and in individual averages showed considerable enhancement of the differences. It was concluded that denoising to remove variance caused by rare sizeable artifacts is best done first, followed by state space PCA and a light-bias model-based wavelet denoising. The ability to detect and distinguish the effects of variables (such as task, drug effects, individual differences, etc.) on ERPs related to human cognition could be considerably advanced using the denoising methods described here.
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P, Karuppusamy. "TECHNIQUES FOR ENHANCEMENT AND DENOISING OF UNDERWATER IMAGES: A REVIEW." Journal of Innovative Image Processing 1, no. 02 (December 10, 2019): 81–90. http://dx.doi.org/10.36548/jiip.2019.2.003.
Повний текст джерелаАнотація:
The images observed from the underwater are usually of low quality because of the scattering of lights, ripples in water and the organic matters resolved in the water. So the preprocessing becomes an important necessity for the images obtained from under water before subjected to the future operations. The various degree of distortions suffered from by the underwater images could be preprocessed by applying the denoising and the image enhancement techniques. The Review addressing the techniques available in enhancing and denoising the underwater images is presented in the paper.
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Wang, Xuan, Liju Yin, Mingliang Gao, Zhenzhou Wang, Jin Shen, and Guofeng Zou. "Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform." Sensors 19, no. 11 (May 29, 2019): 2462. http://dx.doi.org/10.3390/s19112462.
Повний текст джерелаАнотація:
Multi-pixel photon counting detectors can produce images in low-light environments based on passive photon counting technology. However, the resulting images suffer from problems such as low contrast, low brightness, and some unknown noise distribution. To achieve a better visual effect, this paper describes a denoising and enhancement method based on a block-matching 3D filter and a non-subsampled contourlet transform (NSCT). First, the NSCT was applied to the original image and histogram-equalized image to obtain the sub-band low- and high-frequency coefficients. Regional energy and scale correlation rules were used to determine the respective coefficients. Adaptive single-scale retinex enhancement was applied to the low-frequency components to improve the image quality. The high-frequency sub-bands whose line features were best preserved were selected and processed using a symbol function and the Bayes-shrink threshold. After applying the inverse transform, the fused photon counting image was subjected to an improved block-matching 3D filter, significantly reducing the operation time. The final result from the proposed method was superior to those of comparative methods in terms of several objective evaluation indices and exhibited good visual effects and details from the objective impression.
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Zhou, Daxin, Yurong Qian, Yuanyuan Ma, Yingying Fan, Jianeng Yang, and Fuxiang Tan. "Low illumination image enhancement based on multi-scale CycleGAN with deep residual shrinkage." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2383–95. http://dx.doi.org/10.3233/jifs-211664.
Повний текст джерелаАнотація:
Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising.
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Suganthy, M., S. Lakshmi, and S. Palanivel. "Enhancing the Quality of Underwater Images using Fusion of sequential Filters and Dehazing." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 296. http://dx.doi.org/10.14419/ijet.v7i2.24.12067.
Повний текст джерелаАнотація:
Effectively analyzing underwater images and identifying any object under the water has become a difficult task. Generally, the factors affecting underwater images are uneven lighting, low contrast, blunt colors, and characteristics of an object based on absorption and scattering of light. The proposed technique involves applying white balancing and contrast enhancement to the original image. The combination of filters namely homomorphic filtering, wavelet denoising, bilateral filter , adaptive filters are used and applied sequentially on the degraded underwater images. The results obtained showed that the proposed algorithm works well in refining the underwater image attributes. Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) are used to evaluate performance of the algorithm.
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Chen, Xiaohua, Qiang Sheng, and Bhupesh Kumar Singh. "Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm." Computational Intelligence and Neuroscience 2021 (September 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/5970957.
Повний текст джерелаАнотація:
There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the image is introduced to decompose the aerobics image. According to the denoising and decomposition results, the enhancement of aerobics image is realized by the logarithmic image processing (LIP) model and gradient sharpening method. Finally, the aerobics image after the enhancement is classified by a modal symmetry algorithm. Experimental results show that the proposed method has a good denoising effect and high classification efficiency, which shows that the algorithm has significant effectiveness and high application performance.
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Wang, Min, Zhen Li, Xiangjun Duan, and Wei Li. "An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/469350.
Повний текст джерелаАнотація:
This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.
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Ye, Jun, and Xian Zhang. "Hyperspectral Image Denoising via Subspace Low-rank Representation and Spatial‐spectral Total Variation." Journal of Imaging Science and Technology 64, no. 1 (January 1, 2020): 10507–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.1.010507.
Повний текст джерелаАнотація:
Abstract Hyperspectral images (HSIs) acquired actually often contain various types of noise, such as Gaussian noise, impulse noise, and dead lines. On the basis of land covers, the spectral vectors in HSI can be separated into different classifications, which means the spectral space can be regarded as a union of several low-rank (LR) subspaces rather than a single LR subspace. Recently, LR constraint has been widely applied for denoising HSI. However, those LR-based methods do not constrain the intrinsic structure of spectral space. And these methods cannot make better use of the spatial or spectral features in an HSI cube. In this article, a framework named subspace low-rank representation combined with spatial‐spectral total variation regularization (SLRR-SSTV) is proposed for HSI denoising, where the SLRR is introduced to more precisely satisfy the low-rank property of spectral space, and the SSTV regularization is involved for the spatial and spectral smoothness enhancement. An inexact augmented Lagrange multiplier method by alternative iteration is employed for the SLRR-SSTV model solution. Both simulated and real HSI experiment results demonstrate that the proposed method can achieve a state-of-the-art performance in HSI denoising.
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Yang, Jun, Junyang Chen, Jun Li, Shijie Dai, and Yihui He. "An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising." Electronics 12, no. 7 (March 24, 2023): 1544. http://dx.doi.org/10.3390/electronics12071544.
Повний текст джерелаАнотація:
In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the image. In this paper, a Center-Adaptive Median Filter (CAMF) based on YOLOv5 is proposed. Unlike other traditional filtering algorithms, CAMF can adjust its size in real-time according to the current pixel position, the center and the boundary frame of each light spot, and the distance between them. This gives CAMF both a strong noise reduction ability and light spot detail protection ability. In our experiment, the evaluation scores of CAMF for the three indicators Peak Signal-to-Noise Ratio (PSNR), Image Enhancement Factor (IEF), and Structural Similarity (SSIM) were 40.47 dB, 613.28 and 0.939, respectively. The results show that CAMF is superior to other filtering algorithms in noise reduction and light spot protection.
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Jeon, Yeong-Jae, Shin-Eui Park, Keun-A. Chang, and Hyeon-Man Baek. "Signal-to-Noise Ratio Enhancement of Single-Voxel In Vivo 31P and 1H Magnetic Resonance Spectroscopy in Mice Brain Data Using Low-Rank Denoising." Metabolites 12, no. 12 (November 29, 2022): 1191. http://dx.doi.org/10.3390/metabo12121191.
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Magnetic resonance spectroscopy (MRS) is a noninvasive technique for measuring metabolite concentration. It can be used for preclinical small animal brain studies using rodents to provide information about neurodegenerative diseases and metabolic disorders. However, data acquisition from small volumes in a limited scan time is technically challenging due to its inherently low sensitivity. To mitigate this problem, this study investigated the feasibility of a low-rank denoising method in enhancing the quality of single voxel multinuclei (31P and 1H) MRS data at 9.4 T. Performance was evaluated using in vivo MRS data from a normal mouse brain (31P and 1H) and stroke mouse model (1H) by comparison with signal-to-noise ratios (SNRs), Cramer-Rao lower bounds (CRLBs), and metabolite concentrations of a linear combination of model analysis results. In 31P MRS data, low-rank denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared with the original data. In 1H MRS data, the method also improved the SNRs, CRLBs, but it performed better for 31P MRS data with relatively simpler patterns compared to the 1H MRS data. Therefore, we suggest that the low-rank denoising method can improve spectra SNR and metabolite quantification uncertainty in single-voxel in vivo 31P and 1H MRS data, and it might be more effective for 31P MRS data. The main contribution of this study is that we demonstrated the effectiveness of the low-rank denoising method on small-volume single-voxel MRS data. We anticipate that our results will be useful for the precise quantification of low-concentration metabolites, further reducing data acquisition voxel size, and scan time in preclinical MRS studies.
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Chen, Juan, Zhencai Zhu, Haiying Hu, Lin Qiu, Zhenzhen Zheng, and Lei Dong. "A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application." Applied Sciences 13, no. 9 (May 6, 2023): 5749. http://dx.doi.org/10.3390/app13095749.
Повний текст джерелаАнотація:
Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.
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Yoon, Sang Min, Yeon Ju Lee, Gang-Joon Yoon, and Jungho Yoon. "Adaptive Total Variation Minimization-Based Image Enhancement from Flash and No-Flash Pairs." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/319506.
Повний текст джерелаАнотація:
We present a novel approach for enhancing the quality of an image captured from a pair of flash and no-flash images. The main idea for image enhancement is to generate a new image by combining the ambient light of the no-flash image and the details of the flash image. In this approach, we propose a method based on Adaptive Total Variation Minimization (ATVM) so that it has an efficient image denoising effect by preserving strong gradients of the flash image. Some numerical results are presented to demonstrate the effectiveness of the proposed scheme.
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Burhan, Iman Mohammed, Rahman Nahi Abid, Mustafa Abdalkhudhur Jasim, and Refed Adnan Jaleel. "Improved Methods for Mammogram Breast Cancer Using by Denoising Filtering." Webology 19, no. 1 (January 20, 2022): 1481–92. http://dx.doi.org/10.14704/web/v19i1/web19099.
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In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues with normal dense and the tissues that are cancerous. Therefore, mammography images need to be improved in order to accurately identify and diagnose breast cancer. The most typical goals of images enhancement are to remove noise and improve image details. With the aid of mammography image processing techniques, a special data including distinctive characteristics of tumors can be differentiated, this could help distinguish between malignant and benign cancers. This work focuses on removing noise of pepper & salt, improving image to increase the quality of mammography and enhance early detection of breast cancer. A specific approach is employed to do this, including of two phases of image denoising base filtration and one phase to improve contrast. The stages of filtering contain the using of wiener and median filters. The contrast enhancement stage utilizes (CLAHE) which is an abbreviation for contrast limited adaptive histogram equalization. Evaluating the performance is done via contrast histogram for the CLAHE and MSE & PSNF for the filters. The results demonstrate that the work technique is doing better when put to comparison with other approaches in term of low MSE (1.1645) and high PSNR (47.4750). The technique will be assessed with additional kinds of noise for future work.
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Wang, Enning, and Jeff Nealon. "Applying machine learning to 3D seismic image denoising and enhancement." Interpretation 7, no. 3 (August 1, 2019): SE131—SE139. http://dx.doi.org/10.1190/int-2018-0224.1.
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We have trained a supervised deep 3D convolutional neural network (CNN) on marine seismic images for poststack structural seismic image enhancement and noise attenuation. Rather than adding artificial noise to training inputs, the difference in noise levels between the training inputs and labels was created by shot density differences. This design enables the trained CNN to mimic the results and power of stacking to specifically target random and coherent migration artifacts while enhancing low-amplitude reflections. We used field seismic from multiple Gulf of Mexico surveys to train the CNN and the SEG Advanced Modeling (SEAM) phase I synthetic data to evaluate the trained network. The diverse geologic features in the training data are needed to avoid overfitting. The processed outputs of the trained neural network are much cleaner than the inputs, and they highlight geologic structures for easier interpretation. Different scales of geologic structures, from high-resolution faults and diffractors to deep subsalt sediments, are well-preserved by the deep neural network. The trained network can be applied on either prestack gathers or poststack images. The approach is easy to implement and straightforward to parameterize, and it has proven to be an effective and flexible production tool for post-migration data conditioning.
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Li, Huafeng, Xiaoge He, Dapeng Tao, Yuanyan Tang, and Ruxin Wang. "Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning." Pattern Recognition 79 (July 2018): 130–46. http://dx.doi.org/10.1016/j.patcog.2018.02.005.
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Palovcak, Eugene, Daniel Asarnow, Melody G. Campbell, Zanlin Yu, and Yifan Cheng. "Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks." IUCrJ 7, no. 6 (October 24, 2020): 1142–50. http://dx.doi.org/10.1107/s2052252520013184.
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In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.
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Wang, Luna, Liao Yu, Jun Zhu, Haoyu Tang, Fangfang Gou, and Jia Wu. "Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement." Healthcare 10, no. 8 (August 4, 2022): 1468. http://dx.doi.org/10.3390/healthcare10081468.
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Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.
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Tan, Hongjun, Dongxiu Ou, Lei Zhang, Guochen Shen, Xinghua Li, and Yuqing Ji. "Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes." Sensors 22, no. 15 (August 4, 2022): 5835. http://dx.doi.org/10.3390/s22155835.
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Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.
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Jiang, Xiao, Haibin Yu, Yaxin Zhang, Mian Pan, Zhu Li, Jingbiao Liu, and Shuaishuai Lv. "An Underwater Image Enhancement Method for a Preprocessing Framework Based on Generative Adversarial Network." Sensors 23, no. 13 (June 21, 2023): 5774. http://dx.doi.org/10.3390/s23135774.
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This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.
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Shi, Xiangsheng, Xuefei Ning, Lidong Guo, Tianchen Zhao, Enshu Liu, Yi Cai, Yuhan Dong, Huazhong Yang, and Yu Wang. "Memory-Oriented Structural Pruning for Efficient Image Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2245–53. http://dx.doi.org/10.1609/aaai.v37i2.25319.
Повний текст джерелаАнотація:
Deep learning (DL) based methods have significantly pushed forward the state-of-the-art for image restoration (IR) task. Nevertheless, DL-based IR models are highly computation- and memory-intensive. The surging demands for processing higher-resolution images and multi-task paralleling in practical mobile usage further add to their computation and memory burdens. In this paper, we reveal the overlooked memory redundancy of the IR models and propose a Memory-Oriented Structural Pruning (MOSP) method. To properly compress the long-range skip connections (a major source of the memory burden), we introduce a compactor module onto each skip connection to decouple the pruning of the skip connections and the main branch. MOSP progressively prunes the original model layers and the compactors to cut down the peak memory while maintaining high IR quality. Experiments on real image denoising, image super-resolution and low-light image enhancement show that MOSP can yield models with higher memory efficiency while better preserving performance compared with baseline pruning methods.
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Yao, Chao, Shuo Jin, Meiqin Liu, and Xiaojuan Ban. "Dense Residual Transformer for Image Denoising." Electronics 11, no. 3 (January 29, 2022): 418. http://dx.doi.org/10.3390/electronics11030418.
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Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, image compression, image enhancement, etc. Recently, Transformer has been a hot technique, which is widely used to tackle computer vision tasks. However, few Transformer-based methods have been proposed for low-level vision tasks. In this paper, we proposed an image denoising network structure based on Transformer, which is named DenSformer. DenSformer consists of three modules, including a preprocessing module, a local-global feature extraction module, and a reconstruction module. Specifically, the local-global feature extraction module consists of several Sformer groups, each of which has several ETransformer layers and a convolution layer, together with a residual connection. These Sformer groups are densely skip-connected to fuse the feature of different layers, and they jointly capture the local and global information from the given noisy images. We conduct our model on comprehensive experiments. In synthetic noise removal, DenSformer outperforms other state-of-the-art methods by up to 0.06–0.28 dB in gray-scale images and 0.57–1.19 dB in color images. In real noise removal, DenSformer can achieve comparable performance, while the number of parameters can be reduced by up to 40%. Experimental results prove that our DenSformer achieves improvement compared to some state-of-the-art methods, both for the synthetic noise data and real noise data, in the objective and subjective evaluations.
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Huang, Liangliang, Huiyan Jiang, Shaojie Li, Zhiqi Bai, and Jitong Zhang. "Two stage residual CNN for texture denoising and structure enhancement on low dose CT image." Computer Methods and Programs in Biomedicine 184 (February 2020): 105115. http://dx.doi.org/10.1016/j.cmpb.2019.105115.
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Yan, Mengying, Danyang Qin, Gengxin Zhang, Huapeng Tang, and Lin Ma. "Nighttime Image Stitching Method Based on Image Decomposition Enhancement." Entropy 25, no. 9 (August 31, 2023): 1282. http://dx.doi.org/10.3390/e25091282.
Повний текст джерелаАнотація:
Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.
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Zhang, Peng Lin, Zhi Qiang Zhao, and Peng Kong. "Study on Pretreatment Method of X-Ray Real-Time Imaging Digital Image." Advanced Materials Research 815 (October 2013): 854–59. http://dx.doi.org/10.4028/www.scientific.net/amr.815.854.
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X-ray nondestructive testing has a wide range of applications, which in materials testing, food testing, manufacturing, instrumentation, automotive parts and other fields having good performance. The paper mainly deals with low contrast X-ray digital images, image edge blur features and digital image preprocessing techniques of contrast. By a crack image taking geometric transformations, gray-scale transformations and image enhancement processing such as pretreatment technology airspace transforms, getting three options that have been able to effectively realize image denoising and enhancement. These three sets of processing solutions, to some extened, opening the image intensity distribution and making cracks sharper image segmentation is the foundation of subsequentence.
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Ge, Wei, Le Zhang, Weida Zhan, Jiale Wang, Depeng Zhu, and Yang Hong. "A Low-Illumination Enhancement Method Based on Structural Layer and Detail Layer." Entropy 25, no. 8 (August 12, 2023): 1201. http://dx.doi.org/10.3390/e25081201.
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Low-illumination image enhancement technology is a topic of interest in the field of image processing. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the image cannot be guaranteed. In order to solve this problem, this paper proposed a low-illumination enhancement method based on structural and detail layers. Firstly, we designed an SRetinex-Net model. The network is mainly divided into two parts: a decomposition module and an enhancement module. Second, the decomposition module mainly adopts the SU-Net structure, which is an unsupervised network that decomposes the input image into a structural layer image and detail layer image. Afterward, the enhancement module mainly adopts the SDE-Net structure, which is divided into two branches: the SDE-S branch and the SDE-D branch. The SDE-S branch mainly enhances and adjusts the brightness of the structural layer image through Ehnet and Adnet to prevent insufficient or overexposed brightness enhancement in the image. The SDE-D branch is mainly denoised and enhanced with textural details through a denoising module. This network structure can greatly reduce computational costs. Moreover, we also improved the total variation optimization model as a mixed loss function and added structural metrics and textural metrics as variables on the basis of the original loss function, which can well separate the structure edge and texture edge. Numerous experiments have shown that our structure has a more significant impact on the brightness and detail preservation of image restoration.
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KHALDI, KAIS, MONIA TURKI-HADJ ALOUANE, and ABDEL-OUAHAB BOUDRAA. "VOICED SPEECH ENHANCEMENT BASED ON ADAPTIVE FILTERING OF SELECTED INTRINSIC MODE FUNCTIONS." Advances in Adaptive Data Analysis 02, no. 01 (January 2010): 65–80. http://dx.doi.org/10.1142/s1793536910000409.
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In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the longer scale ones unchanged. In fact, the filtering of longer scale IMFs will introduce distortion rather than reducing noise. The denoising method is applied to several voiced speech signals with different noise levels and the results are compared with wavelet approach, ACWA filter and EMD–ACWA (filtering of all IMFs using ACWA filter). Relying on exhaustive simulations, we show the efficiency of the proposed method for reducing noise and its superiority over other denoising methods, i.e. to improve Signal-to-Noise Ratio (SNR), and to offer better listening quality based on a Perceptual Evaluation of Speech Quality (PESQ). The present study is limited to signals corrupted by additive white Gaussian noise.
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Manwar, Rayyan, Matin Hosseinzadeh, Ali Hariri, Karl Kratkiewicz, Shahryar Noei, and Mohammad N. Avanaki. "Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging." Sensors 18, no. 10 (October 17, 2018): 3498. http://dx.doi.org/10.3390/s18103498.
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In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much fewer acquisitions, compared to common averaging techniques. Consequently, PA imaging is conducted considerably faster.
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Guo, Shiyao, Yuxia Sheng, Li Chai, and Jingxin Zhang. "Kernel graph filtering—A new method for dynamic sinogram denoising." PLOS ONE 16, no. 12 (December 2, 2021): e0260374. http://dx.doi.org/10.1371/journal.pone.0260374.
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Low count PET (positron emission tomography) imaging is often desirable in clinical diagnosis and biomedical research, but its images are generally very noisy, due to the very weak signals in the sinograms used in image reconstruction. To address this issue, this paper presents a novel kernel graph filtering method for dynamic PET sinogram denoising. This method is derived from treating the dynamic sinograms as the signals on a graph, and learning the graph adaptively from the kernel principal components of the sinograms to construct a lowpass kernel graph spectrum filter. The kernel graph filter thus obtained is then used to filter the original sinogram time frames to obtain the denoised sinograms for PET image reconstruction. Extensive tests and comparisons on the simulated and real life in-vivo dynamic PET datasets show that the proposed method outperforms the existing methods in sinogram denoising and image enhancement of dynamic PET at all count levels, especially at low count, with a great potential in real life applications of dynamic PET imaging.
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Huang, Hui, Xi’an Feng, and Jionghui Jiang. "Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6807473.
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According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.
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Huang, Shih-Chia, Quoc-Viet Hoang, Trung-Hieu Le, Yan-Tsung Peng, Ching-Chun Huang, Cheng Zhang, Benjamin C. M. Fung, Kai-Han Cheng, and Sha-Wo Huang. "An Advanced Noise Reduction and Edge Enhancement Algorithm." Sensors 21, no. 16 (August 10, 2021): 5391. http://dx.doi.org/10.3390/s21165391.
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Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.
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Dong, Xuan, Xiaoyan Hu, Weixin Li, Xiaojie Wang, and Yunhong Wang. "MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1264–72. http://dx.doi.org/10.1609/aaai.v35i2.16214.
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We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i.e. the final HDR image is fused from pixels of all the input LDR images. However, ghost artifacts can be hardly avoided using this strategy. Instead of directly merging the multiple LDR inputs, we use an indirect way which enhances the main image, i.e. the short exposure image IS, using the long exposure image IL serving as guidance. In detail, we propose a new model, named MIEHDR CNN model, which consists of three subnets, i.e. Soft Warp CNN, 3D Guided Denoising CNN and Fusion CNN. The Soft Warp CNN aligns IL to get the aligned result ILA using the soft exposed result of IS as reference. The 3D Guided Denoising CNN denoises the soft exposed result of IS using ILA as guidance, whose result are fed into the Fusion CNN with IS to get the HDR result. The MIEHDR CNN model is implemented by MindSpore and experimental results show that we can outperform related methods largely and avoid ghost artifacts.
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del Ser, D., and O. Fors. "tfaw survey – I. Wavelet-based denoising of K2 light curves. Discovery and validation of two new Earth-sized planets in K2 campaign 1." Monthly Notices of the Royal Astronomical Society 498, no. 2 (August 19, 2020): 2778–97. http://dx.doi.org/10.1093/mnras/staa2509.
Повний текст джерелаАнотація:
ABSTRACT The wavelet-based detrending and denoising method tfaw is applied for the first time to EVEREST 2.0-corrected light curves to further improve the photometric precision of almost all K2 observing campaigns (C1–C8, C12–C18). The performance of both methods is evaluated in terms of 6 h combined differential photometric precision (CDPP), simulated transit detection efficiency, and planet characterization in different SNR regimes. On average, tfaw median 6 h CDPP is ${\sim} 30{\rm {per \, cent}}$ better than the one achieved by EVEREST 2.0 for all observing campaigns. Using the transit least-squares (tls) algorithm, we show that the transit detection efficiency for simulated Earth–Sun-like systems is ∼8.5× higher for tfaw-corrected light curves than that for EVEREST 2.0 ones. Using the light curves of two confirmed exoplanets, K2-44 b (high SNR) and K2-298 b (low SNR), we show that tfaw yields better Markov chain Monte Carlo posterior distributions, transit parameters compatible with the catalogued ones but with smaller uncertainties, and narrows the credibility intervals. We use the combination of tfaw’s improved photometric precision and tls enhancement of the signal detection efficiency for weak signals to search for new transit candidates in K2 observing campaign 1. We report the discovery of two new K2-C1 Earth-sized planets statistically validated, using the vespa software: EPIC 201170410.02, with a radius of 1.047$^{+0.276}_{-0.257}\mathrm{ R}_{\oplus }$ planet orbiting an M-type star, and EPIC 201757695.02, with a radius of 0.908$^{+0.059}_{-0.064}\mathrm{ R}_{\oplus }$ planet orbiting a K-type star. EPIC 201757695.02 is the 9th smallest planet ever discovered in K2-C1, and the 39th smallest in all K2 campaigns.
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Tsagkatakis, Grigorios, Anastasia Aidini, Konstantina Fotiadou, Michalis Giannopoulos, Anastasia Pentari, and Panagiotis Tsakalides. "Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement." Sensors 19, no. 18 (September 12, 2019): 3929. http://dx.doi.org/10.3390/s19183929.
Повний текст джерелаАнотація:
Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.
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Huo, Fu Rong, He Li, Yu Hang Yang, Chang Xi Xue, and Wen Sheng Wang. "Imaging Analysis and Application of Digital Speckle Photography with EALCD." Applied Mechanics and Materials 333-335 (July 2013): 1007–12. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1007.
Повний текст джерелаАнотація:
According to the principle of speckle photography, CCD(Charge-Coupled Device) as a recorder, and EALCD(Electrically Addressed Liquid Crystal Display) as a read-out element, which makes the speckle photography to digital. Recording light path of the subjective and the objective speckle and observation light path of full-field analysis and point-by-point analysis for fringe reconstruction have been respectively researched. At the same time, measuring by speckle photography, the fringes in the interometry pattern must be carefully analyzed. Since the speckle noise can greatly infect the signals. Thus to de-noising the speckle fringe by a suitable filter before processing is expected. The mathod of digital filter enhancement, smooth denoising be used to reduce the influence of the high noise and the inhomogeneous grey of the speckle interferogram. Finally the thinning fringe is acquired, the fringe spacing is extracted accurately, the three-dimensional measurement of a deformed object is realized with mathematical morphological thinning algorithm.