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1
Ishii, Shin, Sehyung Lee, Hidetoshi Urakubo, Hideaki Kume, and Haruo Kasai. "Generative and discriminative model-based approaches to microscopic image restoration and segmentation." Microscopy 69, no. 2 (March 26, 2020): 79–91. http://dx.doi.org/10.1093/jmicro/dfaa007.
Повний текст джерелаАнотація:
Abstract Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.
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Tang, Yi, Jin Qiu, and Ming Gao. "Fuzzy Medical Computer Vision Image Restoration and Visual Application." Computational and Mathematical Methods in Medicine 2022 (June 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6454550.
Повний текст джерелаАнотація:
In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.
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Zhang, Yang, Hangyu Xie, Shikai Zhuang, and Xiaoan Zhan. "Image Processing and Optimization Using Deep Learning-Based Generative Adversarial Networks (GANs)." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (June 11, 2024): 50–62. http://dx.doi.org/10.60087/jaigs.v5i1.163.
Повний текст джерелаАнотація:
This paper introduces the application of generative adversarial networks (GANs) in image processing and optimization. GANs model can generate realistic images by co-training generator and discriminator, and achieve remarkable results in image restoration tasks. CATGAN and DCGAN are two commonly used GAN models applied to image classification and image restoration respectively. In addition, the global and local image patching methods can effectively fill the missing areas in the image and show good results in the restoration of large images. In conclusion, the image processing and optimization method based on GANs has shown great potential in practice and provides beneficial insight for future research and application in the field of image processing.
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Liu, Zihan. "Literature Review on Image Restoration." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2386/1/012041.
Повний текст джерелаАнотація:
Abstract Image restoration is an essential part in the field of computer vision, which aims at predicting and filling the pixels of the missing images to achieve satisfactory visual effects, it has extensive application value in the fields of film and television special effects production, image editing, digital cultural heritage protection and virtual reality. With the introduction and application of the concept of deep learning in recent years, it has been widely studied in the academic and industrial fields, the performance of image restoration has been significantly improved, so that this long-standing research topic has once again aroused widespread concern and heated discussion on the social level. In order to enable more researchers to explore the theory of image restoration and its development, this paper reviews the related technologies in this field: firstly, the traditional image restoration methods are described, secondly, the background of deep learning is introduced, then the image restoration methods based on deep learning are described, subsequently, the several deep-learning based methods are compared and analyzed, finally, the future research direction and emphasis of image restoration are analyzed and prospected.
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Yuan, Yuan, Yao Hua Yi, and Min Jing Miao. "An Automatic Calculation Method of MTF and the Application in Blurred Images Restoration." Applied Mechanics and Materials 731 (January 2015): 141–46. http://dx.doi.org/10.4028/www.scientific.net/amm.731.141.
Повний текст джерелаАнотація:
Modulation transfer function (MTF) is a comprehensive index for the objective reflection of the quality of the imaging systems. In the field of image processing, the slanted-edge method is usually adopted to compute MTF for images. However, the sub-images with slanted edges are extracted from original image by subjective judgment and manually operation, which is bound to lead to inefficiency of calculation, low accuracy and instability of results with the participation of humans. Aiming at the above problem, this paper presents an automatic MTF calculation method for blurred images and applies it to the process of image restoration by developing a two-dimensional MTF filter and utilizing it into conventional restoration methods such as the Wiener filtering, least squares filtering and Lucy-Richardson algorithm. Experiment results indicate the proposed method achieved an automatic, fast and accurate MTF computation for blurred images, and MTF-based restoration methods were superior to traditional ones in restoration effects.
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Li, Yiyang. "Digital signal processing techniques for image enhancement and restoration." Applied and Computational Engineering 17, no. 1 (October 23, 2023): 198–205. http://dx.doi.org/10.54254/2755-2721/17/20230940.
Повний текст джерелаАнотація:
Digital image processing has become a fundamental tool in modern image processing, including image enhancement and restoration. This paper reviews important image enhancement and restoration techniques in digital image processing. First, some important image enhancement techniques such as histogram equalization are introduced and compared in detail, including their advantages, disadvantages, and application scenarios. Secondly, for image restoration techniques, this paper introduces deblurring techniques such as deconvolution and blind deconvolution, explaining their working principles and application scenarios in detail. Finally, this paper introduces the development and applications of super-resolution technology, and explores their possible future development directions. This review provides comprehensive technical references for researchers in digital image processing.
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Hafiz Muhammad Tayyab Khushi. "Impulse Noise Removal Using Soft-computing." Lahore Garrison University Research Journal of Computer Science and Information Technology 6, no. 1 (March 30, 2022): 32–48. http://dx.doi.org/10.54692/lgurjcsit.2022.0601275.
Повний текст джерелаАнотація:
Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation.
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Kashyap, R. L., and K. B. Eom. "Robust image modeling techniques with an image restoration application." IEEE Transactions on Acoustics, Speech, and Signal Processing 36, no. 8 (1988): 1313–25. http://dx.doi.org/10.1109/29.1659.
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Hu, Yang Bo, Hua Jiang, and Long Bing Li. "The Research of Application in Image Restoration Based on Wiener Filtering." Applied Mechanics and Materials 278-280 (January 2013): 1232–36. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1232.
Повний текст джерелаАнотація:
Image, due to the impact of such as blurring, distortion, noise, etc., will cause a reduction in image quality and the formation of the degradation of the digital image. The using of some kind of a priori knowledge use the least squares method to be filtered, so that the original image and its recovery minimum mean square error between the two images. This can get a better recovery image.
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Tao, Yu, and Jan-Peter Muller. "Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System." Remote Sensing 11, no. 1 (December 29, 2018): 52. http://dx.doi.org/10.3390/rs11010052.
Повний текст джерелаАнотація:
High spatial resolution Earth observation imagery is considered desirable for many scientific and commercial applications. Given repeat multi-angle imagery, an imaging instrument with a specified spatial resolution, we can use image processing and deep learning techniques to enhance the spatial resolution. In this paper, we introduce the University College London (UCL) MAGiGAN super-resolution restoration (SRR) system based on multi-angle feature restoration and deep SRR networks. We explore the application of MAGiGAN SRR to a set of 9 MISR red band images (275 m) to produce up to a factor of 3.75 times resolution enhancement. We show SRR results over four different test sites containing different types of image content including urban and rural targets, sea ice and a cloud field. Different image metrics are introduced to assess the overall SRR performance, and these are employed to compare the SRR results with the original MISR input images and higher resolution Landsat images, where available. Significant resolution improvement over various types of image content is demonstrated and the potential of SRR for different scientific application is discussed.
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Bertero, M., P. Boccacci, and F. Maggio. "Regularization methods in image restoration: An application to HST images." International Journal of Imaging Systems and Technology 6, no. 4 (1995): 376–86. http://dx.doi.org/10.1002/ima.1850060411.
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Sari, Dewi Mutiara, Bayu Sandi Marta, Muhammad Amin A, and Haryo Dwito Armono. "The Analysis of Underwater Imagery System for Armor Unit Monitoring Application." International Journal of Artificial Intelligence & Robotics (IJAIR) 5, no. 1 (April 29, 2023): 1–12. http://dx.doi.org/10.25139/ijair.v5i1.5918.
Повний текст джерелаАнотація:
The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.
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Li, Qiong, Yong Hang Tai, Zai Qing Chen, Qiu Yue Yang, and Bin Zhuo. "Image Pro-Correction for Defocus Blur Image Based on Wiener Filtering." Applied Mechanics and Materials 397-400 (September 2013): 2257–61. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2257.
Повний текст джерелаАнотація:
Image restoration is an important application of the digital image processing. Unlike traditional restoration algorithms that operate on a blurred image to recover the original, we propose a technique that the correction should be applied to the original image before blurring. To accomplish this, we approximate the Point-Spread-Function (PSF) of different defocus blur images by the circular disk. According to the estimated PSF, the original image is pro-processed based on Wiener filtering and High Dynamic Range (HDR) compression. Experiments results show that using this technique can help ameliorate the visual blur and the defocus images finally have a sharp vision.
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COMBETTES, PATRICK L., and JEAN-CHRISTOPHE PESQUET. "WAVELET-CONSTRAINED IMAGE RESTORATION." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 04 (December 2004): 371–89. http://dx.doi.org/10.1142/s0219691304000688.
Повний текст джерелаАнотація:
Image restoration problems can naturally be cast as constrained convex programming problems in which the constraints arise from a priori information and the observation of signals physically related to the image to be recovered. In this paper, the focus is placed on the construction of constraints based on wavelet representations. Using a mix of statistical and convex-analytical tools, we propose a general framework to construct wavelet-based constraints. The resulting optimization problem is then solved with a block-iterative parallel algorithm which offers great flexibility in terms of implementation. Numerical results illustrate an application of the proposed framework.
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Yang, Mengxuan, Shengnan Li, and Jinhua Zeng. "The Effects of AI-Driven Face Restoration on Forensic Face Recognition." Applied Sciences 14, no. 9 (April 29, 2024): 3783. http://dx.doi.org/10.3390/app14093783.
Повний текст джерелаАнотація:
In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification.
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Salah, F. E., N. Moussaid, A. Abassi, and A. Jadir. "Towards a Nash game strategy approach to blind image deconvolution: a fractional-order derivative variational framework." Mathematical Modeling and Computing 11, no. 3 (2024): 682–91. http://dx.doi.org/10.23939/mmc2024.03.682.
Повний текст джерелаАнотація:
Image restoration is a critical process aimed at recovering degraded images, often impacted by factors including motion blur, sensor blurring, defocused photography, optical aberrations, atmospheric distortions, and noise-induced blur. The inherent challenge lies in the typical scenario where both the original image and the blur kernel (Point Spread Function, PSF) are unknown. This restorative process finds applications in various fields, including sensing, medical imaging, astronomy, remote sensing, and criminal investigations. This paper introduces an innovative approach to blind image deconvolution based on Nash game theory. Our focus is placed on restoring linearly corrupted images without processing explicit knowledge of the original image or the blur kernel (PSF). The proposed method formulates blind deconvolution as a two-player static game, with one player dedicated to image deblurring and the other focused on estimating the PSF. The optimal solution is characterized as Nash equilibrium, resulting in effective image restoration. Moreover, we present an enhanced game formulation that incorporates fractional-order derivatives. This unique extension has the potential to improve image restoration accuracy and resilience, leading to breakthroughs in blind image deconvolution and practical applications.
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Gao, Ruoran, Huimin Lu, Adil Al-Azzawi, Yupeng Li, and Chengcheng Zhao. "DRL-FVRestore: An Adaptive Selection and Restoration Method for Finger Vein Images Based on Deep Reinforcement." Applied Sciences 13, no. 2 (January 4, 2023): 699. http://dx.doi.org/10.3390/app13020699.
Повний текст джерелаАнотація:
Finger vein recognition has become a research hotspot in the field of biometrics due to its advantages of non-contact acquisition, unique information, and difficulty in terms of forging or pirating. However, in the real-world application process, the extraction of image features for the biometric remains a significant challenge when the captured finger vein images suffer from blur, noise, or missing feature information. To address the above challenges, we propose a novel deep reinforcement learning-based finger vein image recovery method, DRL-FVRestore, which trained an agent that adaptively selects the appropriate restoration behavior according to the state of the finger vein image, enabling continuous restoration of the image. The behaviors of image restoration are divided into three tasks: deblurring restoration, defect restoration, and denoising and enhancement restoration. Specifically, a DeblurGAN-v2 based on the Inception-Resnet-v2 backbone is proposed to achieve deblurring restoration of finger vein images. A finger vein feature-guided restoration network is proposed to achieve defect image restoration. The DRL-FVRestore is proposed to deal with multi-image problems in complex situations. In this paper, extensive experimental results are conducted based on using four publicly accessible datasets. The experimental results show that for restoration with single image problems, the EER values of the deblurring network and damage restoration network are reduced by an average of 4.31% and 1.71%, respectively, compared to other methods. For images with multiple vision problems, the EER value of the proposed DRL-FVRestore is reduced by an average of 3.98%.
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Sun, Wei. "Cite space-based research on the use of artificial intelligence in the field of literature and museums." Theoretical and Natural Science 34, no. 1 (April 29, 2024): 172–78. http://dx.doi.org/10.54254/2753-8818/34/20241178.
Повний текст джерелаАнотація:
In recent years, with the rapid development and application of artificial intelligence technology, image technology based on artificial intelligence has gradually become a research hotspot in the field of cultural relic restoration. This article will explore the application of artificial intelligence based image technology in the field of cultural relic and museum restoration. Firstly, the importance of restoring cultural relics and artifacts was introduced, as well as the limitations and shortcomings of traditional restoration methods. Secondly, elaborate on the development and application of artificial intelligence technology, as well as its potential and advantages in the field of cultural relic restoration. Next, we will introduce the application of artificial intelligence based image technology in cultural relic restoration, including image enhancement, segmentation, recognition, and reconstruction. Finally, the application prospects and challenges of artificial intelligence based image technology in the field of cultural relic restoration were summarized, and future research directions and suggestions were proposed.
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Tammineni, Shanmukhaprasanthi, Swaraiya Madhuri Rayavarapu, Sasibhushana Rao Gottapu, and Raj Kumar Goswami. "DIGITAL IMAGE RESTORATION USING SURF ALGORITHM." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 1 (March 31, 2024): 37–40. http://dx.doi.org/10.35784/iapgos.5373.
Повний текст джерелаАнотація:
In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.
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Lin, Yu-Yang, Wan-Jen Huang, and Chia-Hung Yeh. "Dual-CycleGANs with Dynamic Guidance for Robust Underwater Image Restoration." Journal of Marine Science and Engineering 13, no. 2 (January 25, 2025): 231. https://doi.org/10.3390/jmse13020231.
Повний текст джерелаАнотація:
The field of underwater image processing has gained significant attention recently, offering great potential for enhanced exploration of underwater environments, including applications such as underwater terrain scanning and autonomous underwater vehicles. However, underwater images frequently face challenges such as light attenuation, color distortion, and noise introduced by artificial light sources. These degradations not only affect image quality but also hinder the effectiveness of related application tasks. To address these issues, this paper presents a novel deep network model for single under-water image restoration. Our model does not rely on paired training images and incorporates two cycle-consistent generative adversarial network (CycleGAN) structures, forming a dual-CycleGAN architecture. This enables the simultaneous conversion of an underwater image to its in-air (atmospheric) counterpart while learning a light field image to guide the underwater image towards its in-air version. Experimental results indicate that the proposed method provides superior (or at least comparable) image restoration performance, both in terms of quantitative measures and visual quality, when compared to existing state-of-the-art techniques. Our model significantly reduces computational complexity, resulting in a more efficient approach that maintains superior restoration capabilities, ensuring faster processing times and lower memory usage, making it highly suitable for real-world applications.
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Ding, Yongsheng, Yunbo Wei, Shuisheng Zhang, and Shihang Yu. "Digital Image Restoration Based on Multicontour Batch Scanning." Scanning 2022 (September 5, 2022): 1–8. http://dx.doi.org/10.1155/2022/8106516.
Повний текст джерелаАнотація:
In order to explore the problem of digital image restoration, the authors propose a research on digital image restoration based on multicontour batch scanning. This method recommends key technical problems and solutions based on information represented by multicontour batch scans, exploring research in digital image restoration. Research has shown that the research on digital image restoration based on multicontour batch scanning is about 40% more efficient than traditional methods. Aiming at the new application of digital image inpainting, the application of image inpainting in image compression is studied in depth, and the technical principles of image inpainting and image compression are complemented.
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Mourtas, Spyridon D. "Color restoration of images through high order zeroing neural networks." ITM Web of Conferences 59 (2024): 01005. http://dx.doi.org/10.1051/itmconf/20245901005.
Повний текст джерелаАнотація:
One of the fundamental tasks in pattern recognition is color image restoration. Every color image has three channels in the RGB color space, in contrast to grayscale images. The restoration of color images is typically far more challenging than that of grayscale images because of the internal relationships among the three channels. Given that the color image restoration can be represented as a dynamic problem with quaternion matrices, a new high order zeroing neural network (HZNN) model is developed to tackle this issue. Specifically, the time-varying quaternion matrix linear equations can be solved using the HZNN design, which is a member of the family of zeroing neural network (ZNN) models that correlate to hyperpower iterative techniques. In a realistic color image restoration application, the HZNN design outperforms the ZNN design, although both approaches work amazingly well.
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Benameur, S., M. Mignotte, J. Meunier, and J. P. Soucy. "Image Restoration Using Functional and Anatomical Information Fusion with Application to SPECT-MRI Images." International Journal of Biomedical Imaging 2009 (2009): 1–12. http://dx.doi.org/10.1155/2009/843160.
Повний текст джерелаАнотація:
Image restoration is usually viewed as an ill-posed problem in image processing, since there is no unique solution associated with it. The quality of restored image closely depends on the constraints imposed of the characteristics of the solution. In this paper, we propose an original extension of the NAS-RIF restoration technique by using information fusion as prior information with application in SPECT medical imaging. That extension allows the restoration process to be constrained by efficiently incorporating, within the NAS-RIF method, a regularization term which stabilizes the inverse solution. Our restoration method is constrained by anatomical information extracted from a high resolution anatomical procedure such as magnetic resonance imaging (MRI). This structural anatomy-based regularization term uses the result of an unsupervised Markovian segmentation obtained after a preliminary registration step between the MRI and SPECT data volumes from each patient. This method was successfully tested on 30 pairs of brain MRI and SPECT acquisitions from different subjects and on Hoffman and Jaszczak SPECT phantoms. The experiments demonstrated that the method performs better, in terms of signal-to-noise ratio, than a classical supervised restoration approach using a Metz filter.
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Wang, Wei, Pei Zhao, Weimin Lei, and Yingjie Ju. "ACMamba: A State Space Model-Based Approach for Multi-Weather Degraded Image Restoration." Electronics 13, no. 21 (October 31, 2024): 4294. http://dx.doi.org/10.3390/electronics13214294.
Повний текст джерелаАнотація:
In computer vision, eliminating the effects of adverse weather conditions such as rain, snow, and fog on images is a key research challenge. Existing studies primarily focus on image restoration for single weather types, while methods addressing image restoration under multiple combined weather conditions remain relatively scarce. Furthermore, current mainstream restoration networks, mostly based on Transformer and CNN architectures, struggle to achieve an effective balance between global receptive field and computational efficiency, limiting their performance in practical applications. This study proposes ACMamba, an end-to-end lightweight network based on selective state space models, aimed at achieving image restoration under multiple weather conditions using a unified set of parameters. Specifically, we design a novel Visual State Space Module (VSSM) and a Spatially Aware Feed-Forward Network (SAFN), which organically combine the local feature extraction capabilities of convolutions with the long-range dependency modeling capabilities of selective state space models (SSMs). This combination significantly improves computational efficiency while maintaining a global receptive field, enabling effective application of the Mamba architecture to multi-weather image restoration tasks. Comprehensive experiments demonstrate that our proposed approach significantly outperforms existing methods for both specific and multi-weather tasks across multiple benchmark datasets, showcasing its efficient long-range modeling potential in multi-weather image restoration tasks.
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Li, Chen. "A Partial Differential Equation-Based Image Restoration Method in Environmental Art Design." Advances in Mathematical Physics 2021 (October 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/4040497.
Повний текст джерелаАнотація:
With the rapid development of networks and the emergence of various devices, images have become the main form of information transmission in real life. Image restoration, as an important branch of image processing, can be applied to real-life situations such as pixel loss in image transmission or network prone to packet loss. However, existing image restoration algorithms have disadvantages such as fuzzy restoration effect and slow speed; to solve such problems, this paper adopts a dual discriminator model based on generative adversarial networks, which effectively improves the restoration accuracy by adding local discriminators to track the information of local missing regions of images. However, the model is not optimistic in generating reasonable semantic information, and for this reason, a partial differential equation-based image restoration model is proposed. A classifier and a feature extraction network are added to the dual discriminator model to provide category, style, and content loss constraints to the generative network, respectively. To address the training instability problem of discriminator design, spectral normalization is introduced to the discriminator design. Extensive experiments are conducted on a data dataset of partial differential equations, and the results show that the partial differential equation-based image restoration model provides significant improvements in image restoration over previous methods and that image restoration techniques are exceptionally important in the application of environmental art design.
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Bhumika Neole. "Application of Mathematical Modelling and Deep Learning in Image Restoration using Edge Preservation Method." Communications on Applied Nonlinear Analysis 31, no. 2s (June 1, 2024): 496–514. http://dx.doi.org/10.52783/cana.v31.663.
Повний текст джерелаАнотація:
The image restoration has witnessed significant advancements with the integration of deep learning techniques with offering unparalleled capabilities in enhancing image quality. This paper study proposed a novel approach to image restoration by incorporating a sophisticated edge preservation method using the deep learning framework. The method aims to address the challenge of preserving high-frequency details, such as edges, while restoring images from various forms of degradation. Also we investigate the use of deep neural networks trained on a different noisy image to restore a clean image with preserving edges of original image. The Deep convolutional neural network (DCNNs), and ResNet50 in deep learning model learns elaborate patterns and features, enabling the reconstruction of images with improved clarity and fidelity. The proposed Deep Convolution Neural Network and ResNet50 methods are designed to restore image content with intelligently preserve and enhance edge information, crucial for maintaining the structural integrity of the original scene. The proposed model is trained on diverse datasets, encompassing a wide range of image degradations, ensuring robust performance across various real-world scenarios. Experimental results demonstrate the efficacy of the proposed approach in comparison to existing methods, showcasing superior edge preservation and overall restoration quality. This research contributes to the advancement of image processing techniques, offering a powerful tool for applications such as medical imaging, satellite imagery, and digital photography, where maintaining fine details is essential for accurate interpretation and analysis.
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Yatnalli, V., B. G. Shivaleelavathi, and K. L. Sudha. "Review of Inpainting Algorithms for Wireless Communication Application." Engineering, Technology & Applied Science Research 10, no. 3 (June 7, 2020): 5790–95. http://dx.doi.org/10.48084/etasr.3547.
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Digital image inpainting is a technique of restoring large removed /damaged regions of an image with the data from the surrounding pixels of the removed region. The issue of image restoration with inpainting techniques occurs commonly in computer vision/image processing when unwanted objects have to be removed from images, for filling cracks in photographs, etc. Digital image inpainting approach is an active field of research in two significant applications of wireless communication: image compression and image recovery from a damaged image due to errors in a wireless channel. This work presents a brief survey of different image inpainting techniques and their contributions to different wireless communication applications.
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Liang, Bo, Xin-xin Jia, and Yuan Lu. "Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design." Complexity 2021 (May 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/9035163.
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Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper.
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Woelk, Lena-Marie, Sukanya A. Kannabiran , Valerie J. Brock , Christine E. Gee , Christian Lohr , Andreas H. Guse , Björn-Philipp Diercks , and René Werner. "Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data." International Journal of Molecular Sciences 22, no. 21 (October 30, 2021): 11792. http://dx.doi.org/10.3390/ijms222111792.
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Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.
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Varkovetski, Michael. "The reduction of directed attention fatigue through exposure to visual nature stimuli: Exploring a natural therapy for fatigue." SURG Journal 8, no. 2 (June 28, 2016): 5–13. http://dx.doi.org/10.21083/surg.v8i2.3057.
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This study compares the restorative effects on directed attention functioning following exposure to natural landscape images versus scrambled/distorted landscape images. Attention restoration theory (ART) provides an analysis of the stimuli and environment required for restoration of cognitive fatigue. According to ART, nature employs attention through a bottom-up process in which intrinsically fascinating stimuli from the natural environment itself modestly dominate attention. This allows the mechanisms responsible for top-down processing, which is necessary for directed attention, to recover and replenish. Unlike natural environments, urban environments employ attention through bottom-up stimulation, which forces one to overcome the stimulation using directed attention, thus not allowing for the recovery of directed attention mechanisms. This study looks into whether solely visual stimulation of natural environments is adequate for the restoration of directed attention mechanisms as measured with the “Attention Test” application. The mean completion time on the Attention Test game was significantly lower in the nature image group (M = 54.33) when compared to the scrambled image group (M = 62.04), thus validating the visual aspect of ART.
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Li, Xu-Chao, Song-Yan Ma, and Wen-Juan Li. "Application of Alternate Iterative Algorithm to Image Restoration." ITM Web of Conferences 11 (2017): 02003. http://dx.doi.org/10.1051/itmconf/20171102003.
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Gluhovsky, Ilya. "Evolutionary Simulated Annealing With Application to Image Restoration." Journal of Computational and Graphical Statistics 13, no. 4 (December 2004): 871–85. http://dx.doi.org/10.1198/106186004x12074.
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Sun, Shuo. "Application of fuzzy image restoration in criminal investigation." Journal of Visual Communication and Image Representation 71 (August 2020): 102704. http://dx.doi.org/10.1016/j.jvcir.2019.102704.
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Su, Zhipeng, Yixiong Zhang, Feng Qi, and Jianghong Shi. "Unsupervised Terahertz Image Restoration Based on CycleGan." Journal of Physics: Conference Series 2478, no. 6 (June 1, 2023): 062027. http://dx.doi.org/10.1088/1742-6596/2478/6/062027.
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Abstract Terahertz (THz) is considered as one of the key technologies for sixth generation communications, military, medical imaging and industrial inspection. THz images are susceptible to degradation due to system noise and point spread functions during transmission. The existing deep learning methods use ground truth and input images for supervised training that can recover THz images very well. But it’s difficult to obtain labeled THz data in practical application. In this paper, we propose an attentional adversarial cycle generation network for THz image restoration (CycleTHz) based on CycleGan to address this problem. The CycleTHz generates clean images firstly by an attention-guided generation network and then discriminates the quality of the generators by an attention discriminator. In addition, RGB color loss is used for image channels for constraint. To the best of our knowledge, this is the first THz dataset to be trained using an unsupervised approach. Extensive experiments show that the proposed method improves the PSNR and SSIM by 43.4% and 101.7% compared with CycleGan, which is a benchmark method for the unsupervised development in THz image restoration. The code is available at https://github.com/hellogry/UnsupervisedCycleTHz
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Fu, Xuhui. "Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (December 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/2691346.
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In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method used to learn the characteristics of sample data. It is a multilayer network, which can learn the information from the bottom to the top of the image through the multilayer network, so as to extract the characteristics of the sample, and then perform identification and classification. The purpose of deep learning is to make the machine have the same analytical and learning capabilities as the human brain. The ability of deep learning in data processing (including images) is unmatched by other methods, and its achievements in recent years have left other methods behind. This article comprehensively reviews the application research progress of deep convolutional neural networks in ancient Chinese pattern restoration and mainly focuses on the research based on deep convolutional neural networks. The main tasks are as follows: (1) a detailed and comprehensive introduction to the basic knowledge of deep convolutional neural and a summary of related algorithms along the three directions of text preprocessing, learning, and neural networks are provided. This article focuses on the related mechanism of traditional pattern repair based on deep convolutional neural network and analyzes the key structure and principle. (2) Research on image restoration models based on deep convolutional networks and adversarial neural networks is carried out. The model is mainly composed of four parts, namely, information masking, feature extraction, generating network, and discriminant network. The main functions of each part are independent and interdependent. (3) The method based on the deep convolutional neural network and the other two methods are tested on the same part of the Qinghai traditional embroidery image data set. From the final evaluation index of the experiment, the method in this paper has better evaluation index than the traditional image restoration method based on samples and the image restoration method based on deep learning. In addition, from the actual image restoration effect, the method in this paper has a better image restoration effect than the other two methods, and the restoration results produced are more in line with the habit of human observation with the naked eye.
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Stanimirović, Predrag S., Igor Stojanović, Vasilios N. Katsikis, Dimitrios Pappas, and Zoran Zdravev. "Application of the Least Squares Solutions in Image Deblurring." Mathematical Problems in Engineering 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/298689.
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A new method for the reconstruction of blurred digital images damaged by separable motion blur is established. The main attribute of the method is based on multiple applications of the least squares solutions of certain matrix equations which define the separable motion blur in conjunction with known image deconvolution techniques. The key feature of the proposed algorithms is reflected in the fact that they can be used only in symbiosis with other image restoration algorithms.
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Nevins, Mandy C., Richard K. Hailstone, and Eric Lifshin. "Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part II: Effect on Image Restoration Quality." Microscopy and Microanalysis 25, no. 05 (August 30, 2019): 1183–94. http://dx.doi.org/10.1017/s1431927619014831.
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AbstractPoint spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.
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Jain, Charu, Anil Kumar, Aarti Chugh, and Nisha Charaya. "Efficient Image Deblurring Application Using Combination of Blind Deconvolution Method and Blur Parameters Estimation Method." ECS Transactions 107, no. 1 (April 24, 2022): 3695–704. http://dx.doi.org/10.1149/10701.3695ecst.
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Image deblurring can be formulated as the inverse process of image blurring. The degraded image can be improved and reconstructed to its original form using image deblurring. Image reconstruction in elementary mode begins with estimation of degradation parameters followed with application of some classical restoration approach. This paper introduces a new approach for image restoration. Here, removal of motion blurs is done through blind deconvolution technique. Two motion parameters angle (θ) and length have been considered for estimation. Hough transform method is used for estimation of blur parameters. PSNR and MSE are employed to exploit important image characteristics, such as denoising, error, randomness, etc. Proposed algorithm is tested for accuracy. Experiments are carried out to study important image features, which play a major role during image restoration.
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Park, Hyosun, Yongsik Jo, Seokun Kang, Taehwan Kim, and M. James Jee. "Deeper, Sharper, Faster: Application of Efficient Transformer to Galaxy Image Restoration." Astrophysical Journal 972, no. 1 (August 23, 2024): 45. http://dx.doi.org/10.3847/1538-4357/ad5954.
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Abstract The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, and time-series forecasting. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We conducted experiments using pairs of high-quality images and their degraded versions, and our deep learning model demonstrates exceptional restoration of photometric, structural, and morphological information. When compared with the ground-truth James Webb Space Telescope images, the enhanced versions of our Hubble Space Telescope–quality images reduce the scatter of isophotal photometry, Sérsic index, and half-light radius by factors of 4.4, 3.6, and 4.7, respectively, with Pearson correlation coefficients approaching unity. The performance is observed to degrade when input images exhibit correlated noise, point-like sources, and artifacts. We anticipate that this deep learning model will prove valuable for a number of scientific applications, including precision photometry, morphological analysis, and shear calibration.
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Cheng, Tongtong, Tingting Bi, Wen Ji, and Chunwei Tian. "Graph Convolutional Network for Image Restoration: A Survey." Mathematics 12, no. 13 (June 28, 2024): 2020. http://dx.doi.org/10.3390/math12132020.
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Image restoration technology is a crucial field in image processing and is extensively utilized across various domains. Recently, with advancements in graph convolutional network (GCN) technology, methods based on GCNs have increasingly been applied to image restoration, yielding impressive results. Despite these advancements, there is a gap in comprehensive research that consolidates various image denoising techniques. In this paper, we conduct a comparative study of image restoration techniques using GCNs. We begin by categorizing GCN methods into three primary application areas: image denoising, image super-resolution, and image deblurring. We then delve into the motivations and principles underlying various deep learning approaches. Subsequently, we provide both quantitative and qualitative comparisons of state-of-the-art methods using public denoising datasets. Finally, we discuss potential challenges and future directions, aiming to pave the way for further advancements in this domain. Our key findings include the identification of superior performance of GCN-based methods in capturing long-range dependencies and improving image quality across different restoration tasks, highlighting their potential for future research and applications.
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Li, Wenxia, Chi Lin, Ting Luo, Hong Li, Haiyong Xu, and Lihong Wang. "Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration." Symmetry 14, no. 3 (March 10, 2022): 558. http://dx.doi.org/10.3390/sym14030558.
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Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.
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Pan, Mingying, and Xiangchu Feng. "Application of Fisher information to CMOS noise estimation." AIMS Mathematics 8, no. 6 (2023): 14522–40. http://dx.doi.org/10.3934/math.2023742.
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<abstract><p>Analysis of the accuracy of estimated parameters is an important research direction. In the article, the maximum likelihood estimation is used to estimate CMOS image noise parameters and Fisher information is used to analyse their accuracy. The accuracies of the two parameters are different in different situations. Two applications of it are proposed in this paper. The first one is a guide to image representation. The standard pixel image has higher accuracy for signal-dependent noise and higher error for additive noise, in contrast to the normalised pixel image. Therefore, the corresponding image representation is chosen to estimate the noise parameters according to the dominant noise. The second application of the conclusions is a guide to algorithm design. For standard pixel images, the error of additive noise estimation will largely affect the final denoising result if two kinds of noise are removed simultaneously. Therefore, a divide-and-conquer hybrid total least squares algorithm is proposed for CMOS image restoration. After estimating the parameters, the total least square algorithm is first used to remove the signal-dependent noise of the image. Then, the additive noise parameters of the processed image are updated by using the principal component analysis algorithm, and the additive noise in the image is removed by BM3D. Experiments show that this hybrid method can effectively avoid the problems caused by the inconsistent precision of the two kinds of noise parameters. Compared with the state-of-art methods, the new method shows certain advantages in subjective visual quality and objective image restoration indicators.</p></abstract>
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Puangpee, Jenwit, and Suthep Suantai. "A New Accelerated Viscosity Iterative Method for an Infinite Family of Nonexpansive Mappings with Applications to Image Restoration Problems." Mathematics 8, no. 4 (April 16, 2020): 615. http://dx.doi.org/10.3390/math8040615.
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The image restoration problem is one of the popular topics in image processing which is extensively studied by many authors because of its applications in various areas of science, engineering and medical image. The main aim of this paper is to introduce a new accelerated fixed algorithm using viscosity approximation technique with inertial effect for finding a common fixed point of an infinite family of nonexpansive mappings in a Hilbert space and prove a strong convergence result of the proposed method under some suitable control conditions. As an application, we apply our algorithm to solving image restoration problem and compare the efficiency of our algorithm with FISTA method which is a popular algorithm for image restoration. By numerical experiments, it is shown that our algorithm has more efficiency than that of FISTA.
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Jia, Peng, Runyu Ning, Ruiqi Sun, Xiaoshan Yang, and Dongmei Cai. "Data-driven image restoration with option-driven learning for big and small astronomical image data sets." Monthly Notices of the Royal Astronomical Society 501, no. 1 (November 13, 2020): 291–301. http://dx.doi.org/10.1093/mnras/staa3535.
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ABSTRACT Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data-driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data-driven image restoration method based on generative adversarial networks with option-driven learning. Our method uses several high-resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.
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Gaikwad, Saurabh Sunil. "Image Colorization." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1242–46. http://dx.doi.org/10.22214/ijraset.2024.61798.
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Abstract: Image colorization is a complex and challenging task in computer vision, with numerous applications in art, entertainment, restoration, and more. This project aims to develop an automated image colorization system leveraging the power of deep learning techniques. The primary objective is to train a deep neural network model capable of accurately and semantically colorizing grayscale images, reproducing natural and visually appealing colour distributions. Our approach utilizes Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to learn the intricate relationships between grayscale images and their corresponding colour versions. The project involves the following key steps: Data Collection and Preprocessing, Model architecture, Training, Evaluation, Application. The project's outcome is expected to provide a powerful and versatile tool for automating image colorization tasks, offering high-quality results while preserving the artistic intent of the original images. The fusion of deep learning and computer vision techniques in this project exemplifies the potential for artificial intelligence to revolutionize image processing and creative industries.
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Wang, Hao-Wen. "Application Methods of Deep Learning in Enhancing Visual Effects in Film and Television Post-production." Journal of Computers 36, no. 1 (February 28, 2025): 191–204. https://doi.org/10.63367/199115992025023601013.
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Post-production of film and television is an important link related to the quality of film and television works. In response to the traditional method of relying on manual post-production of film and television works, this article uses artificial intelligence to improve visual enhancement and video editing of film and television works. Firstly, this article analyzes the current status of post-production in film and television. Three consecutive frames of images are selected from the video sequence image in film and television post-production, and the differential image of adjacent two frames is determined. Logical operations are performed on the two differential binary images to automatically obtain the ROI within the scene of the film and television post-production video image. Then, in order to achieve high-quality video restoration while automatically repairing the computational complexity during the video restoration process, a method of optical flow propagation based on global matching and Transformer encoder is proposed to effectively improve the accuracy of optical flow restoration in videos. Finally, this article takes a certain video clip from daily promotional activities as the experimental object, and uses the artificial intelligence post-production method proposed in this article to improve the visual effect of film and television works.
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Darmawan, Dika Rizki, Fauziah Fauziah, and Ratih Titi Komalasari. "Aplikasi Perbandingan Sistem Perbaikan Citra Digital menggunakan Metode Dekonvolusi Wiener, Lucy Richardson, dan Regularized." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 4, no. 2 (November 17, 2020): 116. http://dx.doi.org/10.35870/jtik.v4i2.154.
Повний текст джерелаАнотація:
In some cases, there is some damage to an image caused by interference during the image capture process. Blurred image damage can be overcome by deconvolution digital image processing. There are various methods to repair the image blur damage, including using the Regularized, Wiener, and Lucy Richardson deconvolution methods. Each blurring image repair method produces a different debluring result of image processing. Image comparison application was built to compare the ability of image restoration results to a Motion Blur image with the algorithms used in deconvolution. Image restoration comparison parameters used include determining the MSE and PSNR values between the test image and the deconvolved image. The results of implementing the comparative application of Motion Blur image improvement to 270 blur simulations consisting of 9 different levels of image blurring, obtained the average PSNR value for Wiener's deconvolution = 59.16dB, Lucy Richardson = 26.92dB and Regularized = 36.94dB.Keywords:Image Restoration; Lucy Richardson; Motion Blur; Regularized; Wiener.
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Sun, Tingzhu, Weidong Fang, Wei Chen, Yanxin Yao, Fangming Bi, and Baolei Wu. "High-Resolution Image Inpainting Based on Multi-Scale Neural Network." Electronics 8, no. 11 (November 19, 2019): 1370. http://dx.doi.org/10.3390/electronics8111370.
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Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail restoration. After holding the visually believable fuzzy texture, we further restore the finer details to produce a smoother, clearer, and more coherent inpainting result. Then we propose a special application scene of image inpainting, that is, to delete the redundant pedestrians in the image and ensure the reality of background restoration. It involves pedestrian detection, identifying redundant pedestrians and filling in them with the seemingly correct content. To improve the accuracy of image inpainting in the application scene, we proposed a new mask dataset, which collected the characters in COCO dataset as a mask. Finally, we evaluated our method on COCO and VOC dataset. the experimental results show that our method can produce clearer and more coherent inpainting results, especially for high-resolution images, and the proposed mask dataset can produce better inpainting results in the special application scene.
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Abduganiev, Mukhriddin, and Sultan Gafurov. "Application of two variable hermite splines in digital image processing." InterConf, no. 34(159) (June 20, 2023): 308–20. http://dx.doi.org/10.51582/interconf.19-20.06.2023.030.
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During the research, the use of piece-polynomial methods in digital processing of images was considered. The Hermite spline function is chosen from piece-polynomials as a mathematical model in digital processing of signals, and the construction of a two-variable third-order Hermite spline function is presented. An image restoration algorithm was developed based on the constructed mathematical model.
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Xiao Hong, Cao Maojun, and Li Panchi. "Quantum-inspired Neural Network with Application to Image Restoration." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 10 (May 31, 2013): 1198–207. http://dx.doi.org/10.4156/aiss.vol5.issue10.140.
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