Cyclic Weighted Median Method for L1 Low-Rank with Nonlocal for Single color image denoising
A challenging problem in computer vision is how to restore clean image from the noisy one. Low rank decomposition based on Li norm has become a popular solution. However, Since the matrix factorization is nonconvex and Li norm is non-smooth, most methods cannot be truly realized and only suboptimal...
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Published in | 2020 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) pp. 370 - 374 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
10.10.2020
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Subjects | |
Online Access | Get full text |
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Summary: | A challenging problem in computer vision is how to restore clean image from the noisy one. Low rank decomposition based on Li norm has become a popular solution. However, Since the matrix factorization is nonconvex and Li norm is non-smooth, most methods cannot be truly realized and only suboptimal results can be obtained. The cyclic weighted median method alleviates this problem to a certain extent by solving a series of scalars minimization convex sub-problems. However, this method currently can only rely on similar image sequences to find low-rank subspaces, which is seriously degraded in single image denoising. In this paper, we introduce non-local self-similar priors, and apply the cyclic weighted median method for single image denoising for the first time. Experiments prove that our method is better than all competition methods. |
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DOI: | 10.1109/CYBER50695.2020.9278965 |