A novel MR image denoising via LRMA and NLSS

Abstract Nonlocal self-similarity has been proven to be a useful tool for image denoising. For MR image denoising, the method combining the nonlocal self-similarity with the low-rank approximation has been recently attracting considerable attentions, due to its favorable performance. Since the original low-rank approximation problem is difficult to be solved, the frequently used method is to use the nuclear norm minimization for the matrix low-rank approximation. However, the solution obtained by nuclear norm minimization generally deviates from the solution of the original problem. In this paper, an approach for MR image denoising is proposed by combining a novel nonlocal self-similarity scheme with a novel low-rank approximation scheme. In proposed approach, a similarity evaluation with respect to the noise is proposed in the patch matching stage. To approximate the original low-rank minimization problem, the propose approach minimizes trace-based operator at each step. Every minimization is solvable and used to approximate the original low-rank minimization. An algorithm is established for this approximation, as well. Experimental results show that the proposed approach has a superior performance, comparing with some of the low-rank approximation methods, in both the objective quality metrics and visual inspections.