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Low Rank and Total Variation Based Two-Phase Method for image deblurring with Salt-and-Pepper Impulse Noise

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Numerical Mathematics(Theory,Methods and Applications) 2023年第4期16卷 1013-1034页

作者： Yuchao Tang Shirong Deng Tieyong Zeng School of Mathematics and Information Science Guangzhou UniversityGuangzhou 510006China Department of Mathematics Nanchang UniversityNanchang 330031China Department of Mathematics The Chinese University of Hong KongShatinHong KongSARChina

Although there are many effective methods for removing impulse noise in image restoration,there is still much room for *** this paper,we propose a new two-phase method for solving such a problem,which combines the nuclear norm and the total variation regularization with box *** popular alternating direction method of multipliers and the proximal alternating direction method of multipliers are employed to solve this *** with other algorithms,the obtained algorithm has an explicit solution at each *** experiments demonstrate that the proposed method performs better than the stateof-the-art methods in terms of both subjective and objective evaluations.

关键词： image deblurring impulse noise total variation nuclear norm

An Energy Regularization Method for the Backward Diffusion Problem and its Applications to image deblurring

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Communications in Computational Physics 2008年第6期4卷 177-194页

作者： Houde Han Ming Yan Chunlin Wu Department of Mathematics University of Science and Technology of ChinaHefeiAnhui 230026China.

For the backward diffusion equation,a stable discrete energy regularization algorithm is *** and uniqueness of the numerical solution are ***,the error between the solution of the given backward diffusion equation and the numerical solution via the regularization method can be *** numerical experiments illustrate the efficiency of the method,and its application in image deblurring.

关键词： Energy regularization method inverse problem heat equation backward diffusion equation image deblurring error estimate ill-posed well-posed.

Incorporating the Maximum Entropy on the Mean Framework with Kernel Error for Robust Non-Blind image deblurring

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Communications in Computational Physics 2022年第3期31卷 893-912页

作者： Hok Shing Wong Hao Zhang Lihua Li Tieyong Zeng Yingying Fang Department of Mathematics The Chinese University of Hong KongHong Kong College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhouChina

Non-blind deblurring is crucial in image *** most previous works assume that the exact blurring kernel is known,this is often not the case in *** blurring kernel is most likely estimated by a blind deblurring method and is not *** this work,we incorporate a kernel error term into an advanced non-blind deblurring method to recover the clear image with an inaccurately estimated *** on the celebrated principle of Maximum Entropy on the Mean(MEM),the regularization at the level of the probability distribution of images is carefully com-bined with the classical total variation regularizer at the level of image/***-sive experiments show clearly the effectiveness of the proposed method in the pres-ence of kernel *** a traditional method,the proposed method is even better than some of the state-of-the-art deep-learning-based *** also demonstrate the potential of combining the MEM framework with classical regularization approaches in image deblurring,which is extremely inspiring for other related works.

关键词： image deblurring total variation KL divergence error kernel

Non-Blind image deblurring Method Using Shear High Order Total Variation Norm

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Wuhan University Journal of Natural Sciences 2021年第6期26卷 495-506页

作者： LU Lixuan ZHANG Tao School of Mathematics and Physics Anhui University of TechnologyMaanshan 243002AnhuiChina Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMaanshan 243002AnhuiChina

In this paper,we propose a shear high-order gradient(SHOG)operator by combining the shear operator and high-order gradient(HOG)*** with the HOG operator,the proposed SHOG operator can incorporate more directionality and detect more abundant edge *** on the SHOG operator,we extend the total variation(TV)norm to shear high-order total variation(SHOTV),and then propose a SHOTV deblurring *** also study some properties of the SHOG operator,and show that the SHOG matrices are Block Circulant with Circulant Blocks(BCCB)when the shear angle isπ/*** proposed model is solved efficiently by the alternating direction method of multipliers(ADMM).Experimental results demonstrate that the proposed method outperforms some state-of-the-art non-blind deblurring methods in both objective and perceptual quality.

关键词： image deblurring high-order TV norm Block Circulant with Circulant Blocks(BCCB)matrix shear operator alternating direction method of multipliers(ADMM)

Remote Sensing image deblurring Based on Grid Computation

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Journal of China University of Mining and Technology 2006年第4期16卷 409-412页

作者： LI Sheng-yang ZHU Chong-guang GE Ping-ju Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China Academy of Opto-Electronics Chinese Academy of Sciences Beijing 100080 China College of Information Science and Engineering Shandong Agricultural University Tai＇an Shandong 271018 China

In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.

关键词： grid computation image deblurring power spectrum equalization remote sensing image

Non-Blind image deblurring via Shear Total Variation Norm

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Wuhan University Journal of Natural Sciences 2024年第3期29卷 219-227页

作者： LI Weiyu ZHANG Tao GAO Qiuli School of Microelectronics and Data Science Anhui University of TechnologyMaanshan 243032AnhuiChina

In this paper, we propose a novel shear gradient operator by combining the shear and gradient operators. The shear gradient operator performs well to capture diverse directional information in the image gradient domain. Based on the shear gradient operator, we extend the total variation(TV) norm to the shear total variation(STV) norm by adding two shear gradient terms. Subsequently, we introduce a shear total variation deblurring model. Experimental results are provided to validate the ability of the STV norm to capture the detailed information. Leveraging the Block Circulant with Circulant Blocks(BCCB) structure of the shear gradient matrices, the alternating direction method of multipliers(ADMM) algorithm can be used to solve the proposed model efficiently. Numerous experiments are presented to verify the performance of our algorithm for non-blind image deblurring.

关键词： image deblurring shear total variation(STV)norm alternating direction method of multipliers(ADMM) Block Circulant with Circulant Blocks(BCCB)matrix

image deblurring Algorithm by using Conditional Generation Adversarial Network

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Image Deblurring Algorithm by using Conditional Generation A...

Generative Adversarial Network for image deblurring Using Content Constraint Loss

第40届中国控制会议

作者： Zhenhua Su Hesheng Zhang Zhongwei Liu Xiaojin Zhu School of Mechatronic Engineering and Automation Shanghai University

deblurring from a motion blurred image has been studied in recent years. Recently, convolution neural network(CNN)has been used widely, which can be used on finding the blur kernel or the latent sharp edge of a blurred image. The last five years, the generative adversarial network(GAN) performs well on style transformation. It is often difficult to maintain a relatively static state between shooting equipment and the photographed object in real life and industrial production, so motion blur is easy to appear in the image taken. To solve this problem, we can use DeblurCGAN to deblur motion blur images. The model can effectively remove the motion blur in moving images in the experimental process. At the same time, compared with the traditional blur kernel, the algorithm not only can get a clearer image but also to restore the texture and detail in the image. Both in the image evaluation index peak signal-to-noise ratio and structural similarity measurement can be verified this method can achieve a better deblurring effect.

关键词： image deblurring DeblurCGAN Generator Discriminator Instance normalization

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Generative Adversarial Network for Image Deblurring Using Co...

image denoising and deblurring: non-convex regularization, inverse diffusion and shock filter

第32届中国控制与决策会议

作者： Ye Ji Yaping Dai Junjie Ma Kaixin Zhao Yanyan Cheng School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

The research of image deblurring plays an important role in the digital image *** order to reduce image blurring problems,a content constraint loss（CCL） function in the generative adversarial network（GAN）is *** SSIM loss and the perceptual loss constitute the CCL function,which makes the trained generative model *** CCL function as the content constraint loss component and the adversarial loss component constitute the total *** total loss is optimized by the iterative training to further improve the stability of the network model,and the image blurring will be *** the test experiment of the open source image dataset MNIST,CIFAR10/100 and CELEBA,the CCL function is used as the content constraint loss component of the generative adversarial network,the effect of image deblurring has obvious promotion in the structural similarity measure and visual appearance.

关键词： image deblurring Generative adversarial network Content constraint loss Adversarial loss

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Science China(Information Sciences) 2011年第6期54卷 1184-1198页

作者： FU ShuJun 1,2,3 , ZHANG CaiMing 2 & TAI XueCheng 3,4 1 School of Mathematics, Shandong University, Jinan 250100, China 2 School of Computer Science and Technology, Shandong University, Jinan 250101, China 3 School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 639798, Singapore 4 Department of Mathematics, University of Bergen, Bergen 5008, Norway School of Mathematics Shandong University Jinan China School of Computer Science and Technology Shandong University Jinan China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Department of Mathematics University of Bergen Bergen Norway

A large number of applications in image processing and computer vision depend on image quality. In this paper, main concerns are image denoising and deblurring simultaneously in a restoration task by three types of methodologies: non-convex regularization, inverse diffusion and shock filter. We discuss their relations in the context of image deblurring: the inverse diffusion implied by the non-convex regularization, and the superior ability of deblurring edge of the shock filter to that of the inverse diffusion, both in 1D and 2D cases. Finally, we propose a region-based adaptive anisotropic diffusion with shock filter method, which shows advantages of deblurring edges, denoising and smoothing contours in experiments, compared with some related methods. Therein an idea of "divide and rule" is introduced.

关键词： image denoising image deblurring non-convex regularization inverse diffusion shock filter adaptive anisotropic diffusion