Image Recovery via Truncated Weighted Schatten-p Norm Regularization

Low-rank prior knowledge has indicated great superiority in the field of image processing. However, how to solve the NP-hard problem containing rank norm is crucial to the recovery results. In this paper, truncated weighted schatten-p norm, which is employed to approximate the rank function by taking advantages of both weighted nuclear norm and truncated schatten-p norm, has been proposed toward better exploiting low-rank property in image CS recovery. At last, we have developed an efficient iterative scheme based on alternating direction method of multipliers to accurately solve the nonconvex optimization model. Experimental results demonstrate that our proposed algorithm is exceeding the existing state-of-the-art methods, both visually and quantitatively.