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Journal articles on the topic 'Denoising'
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1
Liu, Qing Yi. "Application of Wavelet Analysis in Denoising Seismic Data." Applied Mechanics and Materials 530-531 (February 2014): 540–43. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.540.
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The random noise is the kind of noise with wide frequency band in seismic data detected by the optical acceleration sensors. The noises influence and destroy the useful signal of the seismic information. There are a lot of methods to remove noise and one of the standard methods to remove the noise of the signal was the fast Fourier transform (FFT) which was the linear Fourier smoothing. In this paper, the novel denoising method based on wavelet analysis was introduced. The denoising results of seismic data with the noise with FFT method and wavelet analysis method, respectively. SNRs of the signal with noise, FFT denoisng and wavelet analysis denoising are-8.69, -1.13, and 8.27 respectively. The results show that the wavelet analysis method is prior to the traditional denoising method. The resolution of the seismic data improves.
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PEI, W. C., H. Y. LONG, Y. G. LI, H. C. JI, L. WANG, and S. ZHANG. "APPLICATION OF QUANTITATIVE ANALYSIS OF GEAR FAULT IN μ-SVD NOISE REDUCTION ALGORITHM DIAGNOSIS." Latin American Applied Research - An international journal 48, no. 3 (October 9, 2019): 229–33. http://dx.doi.org/10.52292/j.laar.2018.233.
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In order to extract the fault features of mechanical equipment submerged by strong background noise, a general subspace denoising algorithm based on Singular value decomposition (SVD) is used to process the signal, that is, μ-SVD denoising algorithm. This algorithm overcomes the disadvantage of traditional wavelet threshold denoising algorithm, which only deals with the wavelet coefficients point by point and ignores the whole structure of the wavelet coefficients. The traditional SVD denoising algorithm is a special case when Lagrange multiplier μ = 0 in μ-SVD denoising algorithm. μ-SVD denoising algorithm includes filter factor, which can suppress the contribution of singular value dominated by noise contribution to the signal after denoising. The parameter selection method of μ-SVD denoising algorithm is discussed, and the influence of denoising order and Lagrange multiplier on denoising effect is emphatically studied. The test results of gear fault simulation signal and gear fault vibration signal at the early stage show that μ-SVD denoising algorithm is better than traditional SVD denoising algorithm in denoising effect. It can extract gear fault features better under strong background noise.
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R. Tripathi, Mr Vijay. "Image Denoising." IOSR Journal of Engineering 1, no. 1 (November 2011): 84–87. http://dx.doi.org/10.9790/3021-0118487.
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Rissanen, J. "MDL denoising." IEEE Transactions on Information Theory 46, no. 7 (2000): 2537–43. http://dx.doi.org/10.1109/18.887861.
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Olhede, Sofia C. "Hyperanalytic Denoising." IEEE Transactions on Image Processing 16, no. 6 (June 2007): 1522–37. http://dx.doi.org/10.1109/tip.2007.896633.
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Giles, Kendall E., Michael W. Trosset, David J. Marchette, and Carey E. Priebe. "Iterative Denoising." Computational Statistics 23, no. 4 (October 12, 2007): 497–517. http://dx.doi.org/10.1007/s00180-007-0090-8.
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Lin, Fanqiang, Kecheng Chen, Xuben Wang, Hui Cao, Danlei Chen, and Fanzeng Chen. "Denoising stacked autoencoders for transient electromagnetic signal denoising." Nonlinear Processes in Geophysics 26, no. 1 (March 1, 2019): 13–23. http://dx.doi.org/10.5194/npg-26-13-2019.
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Abstract. The transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, which make up an unsupervised learning model in deep learning, on the basis of the analysis of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders) model based on deep neural networks of feature extraction and denoising. SFSDSA maps the signal points of the noise interference to the high-probability points with a clean signal as reference according to the deep characteristics of the signal, so as to realize the signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can (i) effectively reduce the noise of the SFS in contrast with the Kalman, principal component analysis (PCA) and wavelet transform methods and (ii) strongly support the speculation of deeper underground features.
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Bertalmío, Marcelo, and Stacey Levine. "Denoising an Image by Denoising Its Curvature Image." SIAM Journal on Imaging Sciences 7, no. 1 (January 2014): 187–211. http://dx.doi.org/10.1137/120901246.
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He, Sun, and Wang. "Noise Reduction for MEMS Gyroscope Signal: A Novel Method Combining ACMP with Adaptive Multiscale SG Filter Based on AMA." Sensors 19, no. 20 (October 10, 2019): 4382. http://dx.doi.org/10.3390/s19204382.
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In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky–Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system (MEMS) gyroscope signal. The denoising scheme includes preliminary denoising and further denoising. At the preliminary denoising stage, the original gyroscope signal is decomposed into signal modes one by one using ACMP with modified stopping criterion based on mutual information. Useful information is extracted while most noise is discarded in the residue at this stage. Then, AMSGF is proposed to further denoise the signal modes. Sample variance based on AMA is used to adjust the window size of AMSGF adaptively. Practical MEMS gyroscope signal denoising results under different motion conditions show the superior performance of the proposed method over empirical mode decomposition (EMD)-based denoising, discrete wavelet threshold denoising, and variational mode decomposition (VMD)-based denoising. Moreover, AMSGF is proven to gain a better denoising effect than some other common smoothing methods.
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Kaur, Roopdeep, Gour Karmakar, and Muhammad Imran. "Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning." Applied Sciences 13, no. 20 (October 22, 2023): 11560. http://dx.doi.org/10.3390/app132011560.
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In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.
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Ruan, Chengzhi, Dean Zhao, Weikuan Jia, Chen Chen, Yu Chen, and Xiaoyang Liu. "A New Image Denoising Method by Combining WT with ICA." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/582640.
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In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task.
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Niu, Yide. "Use wavelet analysis and other algorithms for the process of image denoising in the mathematical field." Applied and Computational Engineering 35, no. 1 (January 22, 2024): 190–200. http://dx.doi.org/10.54254/2755-2721/35/20230393.
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By using sophisticated mathematical methods in MATLAB, the investigation into the complex field of image noise reduction presented in this paper is thorough. like threshold denoising, correlation denoising, and modal maxima denoising. The use of both manual questionnaires and machine-based evaluation measures to fully evaluate the effectiveness of the denoising algorithms is a key component of this study. This dual strategy, which takes into account both objective and subjective components of image quality enhancement, ensures a well-rounded review. According to our research, correlation denoising consistently proves to be the most practical and effective method across a range of noise types and image categories, although modal maxima denoising and threshold denoising show promising outcomes in some situations. The thorough statistical analysis of our findings supports this conclusion, making it a convincing option for real-world picture denoising applications. In conclusion, this research broadens the range of image denoising techniques, which benefits both the field of image processing and the mathematical community. This study provides a holistic view of image denoising by incorporating wavelet analysis and other sophisticated algorithms, ultimately giving practitioners a deeper understanding of the techniques available for improving image quality in the presence of noise.
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Zhang, Ying, and Fu Cheng You. "Research Progress of Wavelet Denoising Method of Transformer Partial Discharge Signal." Advanced Materials Research 571 (September 2012): 584–88. http://dx.doi.org/10.4028/www.scientific.net/amr.571.584.
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Wavelet analysis has been widely used in the denoising of partial discharge signal of transformer. This paper introduces the main method of partial discharge signal denoising, which focuses on the studying of wavelet denoising methods. The main wavelet denoising methods are introduced herein including wavelet decomposition and reconstruction method, wavelet thresholding method, the translation invariant wavelet thresholding method, the wavelet denoising based on modulus maxima method, and the most widely used wavelet thresholding is introduced primarily. The analysis of their advantages and disadvantages is helpful to choose a proper wavelet denoising method.
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Luo, Xu, Lihong Wang, Shufeng Cao, Qiuhan Xiao, Hongjuan Yang, and Jianguo Zhao. "Signal Processing Methods of Enhanced Magnetic Memory Testing." Processes 11, no. 2 (January 17, 2023): 302. http://dx.doi.org/10.3390/pr11020302.
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As a particular kind of detection technology under weak magnetization, metal magnetic memory testing is very likely to be affected by external factors in the detecting process, which may lead to incorrect results. In order to minimize the negative influence of interrupting signals and improve the detection accuracy, this paper adopted the enhanced metal magnetic memory testing method to preliminarily increase the signal-to-noise ratio (SNR) of the detection signal and then compares the denoising effects of wavelet threshold denoising method, empirical mode decomposition (EMD) denoising method, EMD-wavelet threshold denoising method, ensemble EMD (EEMD), complementary EEMD (CEEMD), variational mode decomposition (VMD), local mean decomposition (LMD) and empirical wavelet transform (EWT) on the detection signal and the gradient signal respectively. The results show that the enhanced metal magnetic memory testing method can significantly increase the SNR of the obtained signal and cannot improve the SNR of a gradient signal which is generated from the obtained signal. The different denoising methods can further boost the SNR and improve the detection accuracy of the obtained signal and the gradient signal. Among the eight signal processing methods, wavelet threshold, EMD and its improved methods are more applicable in the denoising of enhanced metal magnetic memory testing signals. The Wavelet threshold denoising, EMD-wavelet threshold denoising and EEMD denoising all have good denoising effects, and the denoising results to the same signal are analogous.
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Xiao, Xue Mei. "Comparison and Improvements of Image Denoising Based on Wavelet Transform." Applied Mechanics and Materials 740 (March 2015): 644–47. http://dx.doi.org/10.4028/www.scientific.net/amm.740.644.
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Wavelet transform denoising is an important application of wavelet analysis in signal and image processing. Several popular wavelet denoising methods are introduced including the Mallat forced denoising, the wavelet transform modulus maxima method and the nonlinear wavelet threshold denoising method. Their advantages and disadvantages are compared, which may be helpful in selecting the wavelet denoising methods. At the same time, several improvement methods are offered.
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Liu, Caixia, and Li Zhang. "A Novel Denoising Algorithm Based on Wavelet and Non-Local Moment Mean Filtering." Electronics 12, no. 6 (March 20, 2023): 1461. http://dx.doi.org/10.3390/electronics12061461.
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Denoising is the basis and premise of image processing and an important part of image preprocessing. Denoising can effectively improve image quality, which contributes to subsequent image processing such as image segmentation, feature extraction, and so on. In this paper, we propose a novel image denoising method based on wavelet transform and nonlocal moment mean filtering approach (NMM). The noisy image is firstly denoised by a wavelet-based soft-thresholding denoising technique and NMM is then utilized to further eliminate the rest noises. Meanwhile, the fusion of moment invariants increases the robustness of our denoising algorithm due to the invariance of image scaling, translation, and rotation of color moments. Experiments show that our algorithm achieves a better denoising effect compared with some other denoising approaches.
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Ma, Hongqiang, Shiping Ma, Yuelei Xu, and Mingming Zhu. "Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising." Journal of Physics: Conference Series 960 (January 2018): 012033. http://dx.doi.org/10.1088/1742-6596/960/1/012033.
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Komander, Birgit, Dirk A. Lorenz, and Lena Vestweber. "Denoising of Image Gradients and Total Generalized Variation Denoising." Journal of Mathematical Imaging and Vision 61, no. 1 (May 26, 2018): 21–39. http://dx.doi.org/10.1007/s10851-018-0819-8.
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Liu, Yukun, Bowen Wan, Daming Shi, and Xiaochun Cheng. "Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution." Remote Sensing 15, no. 2 (January 6, 2023): 364. http://dx.doi.org/10.3390/rs15020364.
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With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.
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Huang, Tingsheng, Chunyang Wang, and Xuelian Liu. "Depth Image Denoising Algorithm Based on Fractional Calculus." Electronics 11, no. 12 (June 19, 2022): 1910. http://dx.doi.org/10.3390/electronics11121910.
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Depth images are often accompanied by unavoidable and unpredictable noise. Depth image denoising algorithms mainly attempt to fill hole data and optimise edges. In this paper, we study in detail the problem of effectively filtering the data of depth images under noise interference. The classical filtering algorithm tends to blur edge and texture information, whereas the fractional integral operator can retain more edge and texture information. In this paper, the Grünwald–Letnikov-type fractional integral denoising operator is introduced into the depth image denoising process, and the convolution template of this operator is studied and improved upon to build a fractional integral denoising model and algorithm for depth image denoising. Depth images from the Redwood dataset were used to add noise, and the mask constructed by the fractional integral denoising operator was used to denoise the images by convolution. The experimental results show that the fractional integration order with the best denoising effect was −0.4 ≤ ν ≤ −0.3 and that the peak signal-to-noise ratio was improved by +3 to +6 dB. Under the same environment, median filter denoising had −15 to −30 dB distortion. The filtered depth image was converted to a point cloud image, from which the denoising effect was subjectively evaluated. Overall, the results prove that the fractional integral denoising operator can effectively handle noise in depth images while preserving their edge and texture information and thus has an excellent denoising effect.
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An, Yang, Hak Keung Lam, and Sai Ho Ling. "Auto-Denoising for EEG Signals Using Generative Adversarial Network." Sensors 22, no. 5 (February 23, 2022): 1750. http://dx.doi.org/10.3390/s22051750.
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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.
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Jebur, Rusul Sabah, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng, and Ali Al-Naji. "Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm." Technologies 11, no. 4 (August 12, 2023): 111. http://dx.doi.org/10.3390/technologies11040111.
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Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications.
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Lin, Bai Lin. "Technology and Simulation of Image Enhancement Based on Wavelet Threshold." Advanced Materials Research 945-949 (June 2014): 1885–89. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1885.
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This paper has stated the theory of wavelet threshold denoising , combining the theory analysis and simulation results,the paper discusses several kinds of factors which affect the denoising capability in a complete denoising algorithm.That provides the date reference of threshold denoising methods in actual image process.
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Xing, Tianyu, Xiaohao Wang, Kai Ni, and Qian Zhou. "A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT." Sensors 24, no. 4 (February 19, 2024): 1340. http://dx.doi.org/10.3390/s24041340.
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Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified.
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Guo, Zi Yu, and Xiao Bo Zhou. "Research and Optimization on BM3D Denoising Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 3976–79. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3976.
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By grouping up the similar blocks in the picture and Collaborative Filtering, BM3D gets a good denoising effect. But denoising performance declined when the noise enhanced. A reason of poor denoising effect in strong noise was put forward in this paper. Then the denoising ability of BM3D was enhanced by optimizing the parameters.BM3D is proved superior to the traditional filtering denoising algorithm and the optimized BM3D gets a better effect than the original one in strong noise in simulation.
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Zhou, Jun, mei Yang, and Yue Yin. "Research on Acoustic Emission Signal Denoising Based on Autoencoder." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2031/1/012001.
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Abstract Acoustic emission signal denoising is the premise of acoustic emission signal identification. The traditional filter and wavelet analysis have the problems of relying on prior information and poor adaptability in acoustic emission signal denoising. Therefore, a noise denoising model of acoustic emission signal based on noise reduction autoencoder is proposed. By unsupervised learning training, the noise reduction autoencoder has more stable invariant characteristics, so that the error between the reconstructed signal and the original signal converges to a minimum, thus achieving the purpose of denoising. The denoising experiments are carried out on the basis of processing 3000 corrosive acoustic emission signal samples. The experimental results show that the model has better denoising effect when the number of hidden layer neurons is 400. The proposed DAE model has better performance and robustness than Donoho wavelet threshold denoising method.
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Lee, Seung-Jun, and Hyuk-Yoon Kwon. "A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection." Applied Sciences 10, no. 20 (October 21, 2020): 7385. http://dx.doi.org/10.3390/app10207385.
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In this paper, we propose a preprocessing strategy for denoising of speech data based on speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, it becomes more important as the deep learning-based methods have been developed because they require significant costs while showing high performance in general. The basic idea of the proposed method is using the speech segment detection so as to exclude non-speech segments before denoising. The speech segmentation detection can exclude non-speech segments with a negligible cost, which will be removed in denoising process with a much higher cost, while maintaining the accuracy of denoising. First, we devise a framework to choose the best preprocessing method for denoising based on the speech segment detection for a target environment. For this, we speculate the environments for denoising using different levels of signal-to-noise ratio (SNR) and multiple evaluation metrics. The framework finds the best speech segment detection method tailored to a target environment according to the performance evaluation of speech segment detection methods. Next, we investigate the accuracy of the speech segment detection methods extensively. We conduct the performance evaluation of five speech segment detection methods with different levels of SNRs and evaluation metrics. Especially, we show that we can adjust the accuracy between the precision and recall of each method by controlling a parameter. Finally, we incorporate the best speech segment detection method for a target environment into a denoising process. Through extensive experiments, we show that the accuracy of the proposed scheme is comparable to or even better than that of Wavenet-based denoising, which is one of recent advanced denoising methods based on deep neural networks, in terms of multiple evaluation metrics of denoising, i.e., SNR, STOI, and PESQ, while it can reduce the denoising time of the Wavenet-based denoising by approximately 40–50% according to the used speech segment detection method.
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Zhang, Xiangning, Yan Yang, and Lening Lin. "Edge-aware image denoising algorithm." Journal of Algorithms & Computational Technology 13 (October 30, 2018): 174830181880477. http://dx.doi.org/10.1177/1748301818804774.
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The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external information needs the support of similar images or image patches. In this paper, an edge-aware image denoising algorithm is proposed to achieve the goal of preserving the details of original image while denoising and using only the characteristics of the noisy image. In general, image denoising algorithms use the noise prior to set parameters todenoise the noisy image. In this paper, it is found that the details of original image can be better preserved by combining the prior information of noise and the image edge features to set denoising parameters. The experimental results show that the proposed edge-aware image denoising algorithm can effectively improve the performance of block-matching and 3D filtering and patch group prior-based denoising algorithms and obtain higher peak signal-to-noise ratio and structural similarity values.
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You, F. C., and Y. Zhang. "An Improved Wavelet Threshold Denoising Method for Transformer Partial Discharge Signal." Applied Mechanics and Materials 214 (November 2012): 148–53. http://dx.doi.org/10.4028/www.scientific.net/amm.214.148.
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In order to overcome the discontinuance of the hard thresholding function and the defect of slashing singularity more seriously in the soft thresholding function, and improve the denoising effect and detect the transformer partial discharge signal more accurately, this paper puts forward an improved wavelet threshold denoising method through analyzing the interference noise of transformer partial discharge signals and studying various wavelet threshold denoising method, especially the wavelet threshold denoising method that overcomes the shortcomings of the hard and soft threshold. Simulation results show that the denoising effect of the method has been greatly improved than the traditional hard and soft threshold method. This method can be widely used in practical transformer partial discharge signal denoising.
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Zhou, Zhi, Xing Man Yang, and Gang Chen. "A Denoising Method Based on EEMD and Interval-Thresholding Strategy." Advanced Materials Research 902 (February 2014): 336–40. http://dx.doi.org/10.4028/www.scientific.net/amr.902.336.
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As a conventional signal denoising method, wavelet thresholding denoising has problems including selection of basis vectors and poor denoising effect. EMD is an expansion of basis functions that are signal-dependent, but with the problem of mode mixing. In order to solve these problems, a denoising method based on EEMD and interval-thresholding strategy, an adaptive signal processing method is proposed, which can achieve good effects for signal denoising. Firstly, investigated signal is decomposed into IMFs by EEMD adaptively. Then, each IMF is denoising by interval-thresholding method based on sparse code shrinkage. Lastly, the denoised signal is reconstructed by denoised IMFs. Moreover, the presented method is validated by numerical simulation experiment.
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Si, Shuming, Han Hu, Yulin Ding, Xuekun Yuan, Ying Jiang, Yigao Jin, Xuming Ge, Yeting Zhang, Jie Chen, and Xiaocui Guo. "Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR." Remote Sensing 15, no. 1 (January 2, 2023): 269. http://dx.doi.org/10.3390/rs15010269.
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Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on the sparsity assumption of point cloud noise, which does not hold for SPL point clouds, so the existing denoising methods cannot effectively remove the noisy points from SPL point clouds. To solve the above problems, we proposed a novel multistage denoising strategy with fused multiscale features. The multiscale features were fused to enrich contextual information of the point cloud at different scales. In addition, we utilized multistage denoising to solve the problem that a single-round denoising could not effectively remove enough noise points in some areas. Interestingly, the multiscale features also prevent an increase in false-alarm ratio during multistage denoising. The experimental results indicate that the proposed denoising approach achieved 97.58%, 99.59%, 95.70%, and 77.92% F1-scores in the urban, suburban, mountain, and water areas, respectively, and it outperformed the existing denoising methods such as Statistical Outlier Removal. The proposed approach significantly improved the denoising precision of airborne point clouds from single-photon LiDAR, especially in water areas and dense urban areas.
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Tang, Yan. "Denoising Processing Technology for Remote Classimage Transmission Based on Cloud Computing." Applied Mechanics and Materials 644-650 (September 2014): 4649–52. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4649.
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Denoising processing technology for remote classroom image transmission is studied. In the traditional method of image denoising, the image after denoisinghave obvious spots, communication is not ideal, noise variance estimation is required in practical applications. This paper presents a MCMC image denoising algorithm based on multi-core, throughremote image transmission fusion method toachieve fast denoising process for remote classroom image transmission. Experimental results show that the algorithm improves the operational efficiency, reduces speckle noise, so that remote image is clearer, and denoising effect is very satisfactory.
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Khan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman, and Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising." Sensors 21, no. 9 (April 24, 2021): 2998. http://dx.doi.org/10.3390/s21092998.
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Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.
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Zeng, Xuyang, Ying Jia, Shuqi Zhao, Guo Xu, Tianyu Ma, and Dianqi Song. "Study on Noise Reduction of Acoustic Emission Signals based on Improved Wavelet Thresholding." Scientific Journal of Technology 6, no. 3 (March 21, 2024): 1–9. http://dx.doi.org/10.54691/dj1a6b05.
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The wavelet transform is extensively utilized in signal denoising due to its benefits of reduced entropy, multiple resolutions, and decorrelation. This paper presents an enhanced wavelet threshold denoising algorithm that combines the existing improved threshold function and threshold selection method, building upon the traditional wavelet threshold denoising algorithm. The enhanced threshold function exhibits improved smoothness and reduced coefficient variation; the novel threshold selection approach integrates the Lipschitz properties of the signal and achieves a higher rate of noise signal elimination. The simulation experiments on denoising demonstrate that the enhanced wavelet threshold denoising algorithm enhances the signal-to-noise ratio (SNR) and mean-square error (MSE) by 14.4% and 58.3% respectively, in comparison to the conventional algorithm. Additionally, it outperforms existing algorithms by 8.4% and 36.5%, showcasing its superior denoising capabilities. These findings validate the performance benefits and practical value of the denoising algorithm proposed in this research paper.
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Duan, Lian Fei, Chuan Ting Wei, Jing Wang, and Yuan Wen Dai. "A New Method of Denoising by Reserving Edges for SAR Image." Applied Mechanics and Materials 291-294 (February 2013): 2859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2859.
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Aimed at the problem of image blur while denoising in SAR image, a new denoising method by reserving edges based on MSP-ROA operator is presented after analyzing the image edge detection. The SAR image edges are well reserved while denoising for combining denoising with the edge detection. Experiment results show that the method is feasible and effective.
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Liang, Xin He, Jin Liang, and Chen Guo. "Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood." Advanced Materials Research 97-101 (March 2010): 3631–36. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3631.
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We present a scatter point cloud denoising method, which can reduce noise effectively, while preserving mesh features such as sharp edges and corners. The method consists of two stages. Firstly, noisy points normal are filtered iteratively; second, location noises of points are reduced. How to select proper denoising neighbors is a key problem for scatter point cloud denoising operation. The local shape factor which related to the surface feature is proposed. By using the factor, we achieved the shape adaptive angle threshold and adaptive optimal denoising neighbor. Normal space and location space is denoising using improved trilateral filter in adaptive angle threshold. A series of numerical experiment proved the new denoising algorithm in this paper achieved more detail feature and smoother surface.
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Yang, Kun, Haojie Zhang, Yufei Qiu, Tong Zhai, and Zhiguo Zhang. "Self-Supervised Joint Learning for pCLE Image Denoising." Sensors 24, no. 9 (April 30, 2024): 2853. http://dx.doi.org/10.3390/s24092853.
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Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements in deep learning offer promise in image denoising, but the acquisition of clean-noisy image pairs for training networks across all potential scenarios can be prohibitively costly. Few studies have explored training denoising networks on such pairs. Here, we propose an innovative self-supervised denoising method. Our approach integrates noise prediction networks, image quality assessment networks, and denoising networks in a collaborative, jointly trained manner. Compared to prior self-supervised denoising methods, our approach yields superior results on pCLE images and fluorescence microscopy images. In summary, our novel self-supervised denoising technique enhances image quality in pCLE diagnosis by leveraging the synergy of noise prediction, image quality assessment, and denoising networks, surpassing previous methods on both pCLE and fluorescence microscopy images.
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Li, Shu, Xi Yang, Haonan Liu, Yuwei Cai, and Zhenming Peng. "Seismic Data Denoising Based on Sparse and Low-Rank Regularization." Energies 13, no. 2 (January 13, 2020): 372. http://dx.doi.org/10.3390/en13020372.
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Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two classes of methods for seismic denoising. One is based on the sparsity of seismic data. This kind of method can make use of the sparsity of seismic data in local area. The other is based on nonlocal self-similarity, and it can utilize the spatial information of seismic data. Sparsity and nonlocal self-similarity are important prior information. However, there is no seismic denoising method using both of them. To jointly use the sparsity and nonlocal self-similarity of seismic data, we propose a seismic denoising method using sparsity and low-rank regularization (called SD-SpaLR). Experimental results showed that the SD-SpaLR method has better performance than the conventional wavelet denoising and total variation denoising. This is because both the sparsity and the nonlocal self-similarity of seismic data are utilized in seismic denoising. This study is of significance for designing new seismic data analysis, processing and inversion methods.
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Kim, Bae-Guen, Seong-Hyeon Kang, Chan Rok Park, Hyun-Woo Jeong, and Youngjin Lee. "Noise Level and Similarity Analysis for Computed Tomographic Thoracic Image with Fast Non-Local Means Denoising Algorithm." Applied Sciences 10, no. 21 (October 23, 2020): 7455. http://dx.doi.org/10.3390/app10217455.
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Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algorithm and conventional denoising filters, such as the Gaussian, median, and Wiener filters, were applied to the MASH phantom image adding Gaussian noise with a standard deviation of 0.002 and practical CT images. Finally, the results were compared quantitatively in terms of the coefficient of variation (COV), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC). The results showed that the FNLM denoising algorithm was more efficient than the conventional denoising filters. In conclusion, through the simulation study and the practical study, this study demonstrated the feasibility of the FNLM denoising algorithm for noise reduction from thoracic CT images.
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Eklund, Anders, Mats Andersson, and Hans Knutsson. "True 4D Image Denoising on the GPU." International Journal of Biomedical Imaging 2011 (2011): 1–16. http://dx.doi.org/10.1155/2011/952819.
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The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 × 512 × 445 × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly.
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Zhou, Yongjun, Huiliang Cao, and Tao Guo. "A Hybrid Algorithm for Noise Suppression of MEMS Accelerometer Based on the Improved VMD and TFPF." Micromachines 13, no. 6 (May 31, 2022): 891. http://dx.doi.org/10.3390/mi13060891.
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High-G MEMS accelerometer (HGMA) is a new type of sensor; it has been widely used in high precision measurement and control fields. Inevitably, the accelerometer output signal contains random noise caused by the accelerometer itself, the hardware circuit and other aspects. In order to denoise the HGMA’s output signal to improve the measurement accuracy, the improved VMD and TFPF hybrid denoising algorithm is proposed, which combines variational modal decomposition (VMD) and time-frequency peak filtering (TFPF). Firstly, VMD was optimized by the multi-objective particle swarm optimization (MOPSO), then the best decomposition parameters [kbest,abest] could be obtained, in which the permutation entropy (PE) and fuzzy entropy (FE) were selected for MOPSO as fitness functions. Secondly, the accelerometer voltage output signals were decomposed by the improved VMD, then some intrinsic mode functions (IMFs) were achieved. Thirdly, sample entropy (SE) was introduced to classify those IMFs into information-dominated IMFs or noise-dominated IMFs. Then, the short-window TFPF was selected for denoising information-dominated IMFs, while the long-window TFPF was selected for denoising noise-dominated IMFs, which can make denoising more targeted. After reconstruction, we obtained the accelerometer denoising signal. The denoising results of different denoising algorithms in the time and frequency domains were compared, and SNR and RMSE were taken as denoising indicators. The improved VMD and TFPF denoising method has a smaller signal distortion and stronger denoising ability, so it can be adopted to denoise the output signal of the High-G MEMS accelerometer to improve its accuracy.
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Zhang, Pengfei, Xinpeng Pan, and Jiawei Liu. "Denoising Marine Controlled Source Electromagnetic Data Based on Dictionary Learning." Minerals 12, no. 6 (May 28, 2022): 682. http://dx.doi.org/10.3390/min12060682.
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Marine controlled source electromagnetic (CSEM) is an efficient method to explore ocean resources. The amplitudes of marine CSEM signals decay rapidly with the measuring offsets. The signal is easily contaminated by various kinds of noise when the offset is large. These noise include instrument internal noise, dipole vibration noise, seawater motion noise and environmental noise Suppressing noise is the key to improve data quality and interpretation accuracy. Sparse representation based denoising method has been used for denoising for a long time. provides a new way to remove noise. Under the framework of sparse representation, the denoising effect is closely related to the chosen transform matrix. This matrix is called dictionary and its column named atom. In general, the stronger the correlation between signal and dictionary is, the sparser representation will be, and further the better the denoising effect will be. In this article, a new method based on dictionary learning is proposed for marine CSEM denoising. Firstly, the signal segments suffering little from noise are captured to compose the training set. Then the learned dictionary is trained from the training set via K-singular value decomposition (K-SVD) algorithm. Finally, the learned dictionary is used to sparsely represent the contaminated signal and reconstruct the filtered one. The effectiveness of the proposed approach is verified by a synthetic data denoising experiment, in which windowed-Fourier-transform (WFT) and wavelet-transform (WT) denoising methods and three dictionaries (discrete-sine-transform (DST) dictionary, DST-wavelet merged dictionary and the learned dictionary) under a sparse representation framework are tested. The results demonstrate the superiority of the proposed dictionary-learning-based denoising method. Finally, the proposed approach is applied to field data denoising process, coupled with DST and DST-wavelet dictionaries based denoising methods. The outcomes further proves that the propsoed approach is effective and superior for marine CSEM data denoising.
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Xu, Shaoping, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, and Nan Xiao. "Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior." Applied Sciences 12, no. 21 (October 24, 2022): 10767. http://dx.doi.org/10.3390/app122110767.
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Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the training data. Recently, several unsupervised learning models have been proposed to reduce the dependence on training data. Although unsupervised methods only require noisy images for learning, their denoising effect is relatively weak compared with supervised methods. This paper proposes a two-stage unsupervised deep learning framework based on deep image prior (DIP) to enhance the image denoising performance. First, a two-target DIP learning strategy is proposed to impose a learning restriction on the DIP optimization process. A cleaner preliminary image, together with the given noisy image, was used as the learning target of the two-target DIP learning process. We then demonstrate that adding an extra learning target with better image quality in the DIP learning process is capable of constraining the search space of the optimization process and improving the denoising performance. Furthermore, we observe that given the same network input and the same learning target, the DIP optimization process cannot generate the same denoised images. This indicates that the denoised results are uncertain, although they are similar in image quality and are complemented by local details. To utilize the uncertainty of the DIP, we employ a supervised denoising method to preprocess the given noisy image and propose an up- and down-sampling strategy to produce multiple sampled instances of the preprocessed image. These sampled instances were then fed into multiple two-target DIP learning processes to generate multiple denoised instances with different image details. Finally, we propose an unsupervised fusion network that fuses multiple denoised instances into one denoised image to further improve the denoising effect. We evaluated the proposed method through extensive experiments, including grayscale image denoising, color image denoising, and real-world image denoising. The experimental results demonstrate that the proposed framework outperforms unsupervised methods in all cases, and the denoising performance of the framework is close to or superior to that of supervised denoising methods for synthetic noisy image denoising and significantly outperforms supervised denoising methods for real-world image denoising. In summary, the proposed method is essentially a hybrid method that combines both supervised and unsupervised learning to improve denoising performance. Adopting a supervised method to generate preprocessed denoised images can utilize the external prior and help constrict the search space of the DIP, whereas using an unsupervised method to produce intermediate denoised instances can utilize the internal prior and provide adaptability to various noisy images of a real scene.
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Yuan, De Bao, Xi Min Cui, Guo Wang, Jing Jing Jin, and Wan Yang Xu. "Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method." Advanced Materials Research 446-449 (January 2012): 926–36. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.926.
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Signal denoising is one of the classic problems in the field of signal processing. As a new kind of signal processing tool, the good denoising performance of wavelet analysis has caused public growing concern and attention. The paper does systematic research on nonlinear wavelet threshold denoising method. And the wavelet denoising method has been used on GPS signal, and good results have been achieved.
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Xiao, Qi Jun, Zhong Hui Luo, and Jun Lan Wu. "Study on Wavelet Packet Denoising Technique and its Application in Mechanical Fault Diagnosis." Applied Mechanics and Materials 713-715 (January 2015): 445–48. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.445.
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In this paper, the author introduces the principles of wavelet packet denoising, conducts a simulated analysis on the improved performance of wavelet packet denoising and develops the source Matlab program. In addition, the measured acoustic signals of seafloor sediments are denoised using wavelet packet. It is feasible to apply the wavelet packet denoising technique in a wide range of engineering testing fields involving denoising operation.
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Wang, Li Di, Jiang Feng Tang, and Jun Sheng Shi. "Parameter Identification for the Dynamic Load Modeling Based on Denoising Method of the Measurement Data." Advanced Materials Research 217-218 (March 2011): 907–10. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.907.
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Different denoising methods are used in parameter identification for the dynamic load modeling and the specific approach is proposed. The effects of different denoising methods including mean filtering, medial filtering and wavelet denoising are discussed. Mean filtering method is not helpful to contain the step changes of the measurement voltage, thus is unsuitable for the parameter identification process. Medial filtering method and wavelet denoising methods are suitable for the parameter identification in dynamic load modeling. Furthermore, experiment results based on the measurement data show that the wavelet denoising method is more efficient in some aspects such as the accuracy of identification and SSE.
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Zhang, Wen-Li, Jing-Yue Zheng, Kun Liang, Ke-Fan Chen, Jian-Hai Zhao, Jian-Qiang Liu, Yi Wang, and Yu-Xin Qin. "Research on Block Matching Three-Dimensional Cooperative Filtering Optical Image Denoising Algorithm Based on Noise Estimation." Journal of Nanoelectronics and Optoelectronics 16, no. 11 (November 1, 2021): 1711–19. http://dx.doi.org/10.1166/jno.2021.3132.
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Optical image denoising is one of the important means to improve the overall clarity of the image or highlight the texture details of the target. However, studies have found that the existing optical image denoising algorithms have a good effect on the images with known noise levels, and have average denoising effects on random noising. Therefore, a novel BM3D optical image denoising algorithm based on noise estimation is proposed. Firstly, the wavelet packet transform is used for the noise estimation of the optical image. Then, according to the noise estimation result, BM3D is used to realize the denoising analysis of the optical image. Finally, PSNR is used to evaluate the effectiveness of the algorithm. Experiments show that the proposed algorithm not only achieves effective denoising of optical image, but also has strong robustness.
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He, Can, Jianchun Xing, Juelong Li, Qiliang Yang, and Ronghao Wang. "A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/280251.
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Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions.
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Nex, F., and M. Gerke. "Photogrammetric DSM denoising." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 231–38. http://dx.doi.org/10.5194/isprsarchives-xl-3-231-2014.
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Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers. These problems constitute a limitation in the practical use of photogrammetric data for many applications but an effective way to enhance the generated point cloud has still to be found. <br><br> In this paper we concentrate on the restoration of Digital Surface Models (DSM), computed from dense image matching point clouds. A photogrammetric DSM, i.e. a 2.5D representation of the surface is still one of the major products derived from point clouds. Four different algorithms devoted to DSM denoising are presented: a standard median filter approach, a bilateral filter, a variational approach (TGV: Total Generalized Variation), as well as a newly developed algorithm, which is embedded into a Markov Random Field (MRF) framework and optimized through graph-cuts. The ability of each algorithm to recover the original DSM has been quantitatively evaluated. To do that, a synthetic DSM has been generated and different typologies of noise have been added to mimic the typical errors of photogrammetric DSMs. The evaluation reveals that standard filters like median and edge preserving smoothing through a bilateral filter approach cannot sufficiently remove typical errors occurring in a photogrammetric DSM. The TGV-based approach much better removes random noise, but large areas with outliers still remain. Our own method which explicitly models the degradation properties of those DSM outperforms the others in all aspects.
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Hou, Wenguang, Taiwai Chan, and Mingyue Ding. "Denoising point cloud." Inverse Problems in Science and Engineering 20, no. 3 (April 2012): 287–98. http://dx.doi.org/10.1080/17415977.2011.603087.
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