The application of low rank matrix decomposition method in image restoration

Abstract

Image restoration is a hot issue in the field of image processing. Traditional algorithms approach the original function by a regular function or remove redundant noise by similarity. This process is complicated and cumbersome. In this paper, the low rank approximate matrix of the image matrix is equivalent to the product of two smaller matrices. At the same time, the first-order and second-order statistical information of the image matrix is effectively maintained by using the matrix Frobenius norm and matrix kernel normal. Secondly, the alternating direction multiplier method is utilized to solve the model. Finally, experimental results test the effectiveness of the proposed algorithm.