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

查看全文 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

出　　处：《Science China(Information Sciences)》索引2011年第54卷第6期,共15页

基　　金：supported by the MOE(Ministry of Education)Tier II Project(Grant No.T207N2202);the IDM Project(Grant No.NRF2007IDM-IDM002-010);the National Natural Science Foundation of China(Grant Nos.60933008,61070094,61020106001);the China Postdoctoral Science Foundation(Grant No.20090460089);the support from SUG20/07 摘　　要：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：图像去噪扩散能力凸正规化去模糊滤波器各向异性扩散计算机视觉图像质量

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

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