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 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.