Matrix-value Linear Regression for Image Denoising

Abstract

Since images (or patches) can be inherently represented by matrices, in this paper, the image denosing task is addressed into learning global linear operator by using matrix-value linear regression strategy. From given training noisy images (or patches) and their corresponding clear ones, a series of linear mappings are potentially defined by those image pairs, and then integrated as a global linear operator on training set. Matrix-value linear regression can be employed to learn the global linear operator. The proposed algorithm can commendably overcome the disadvantages of the vector-based methods. Empirical experiments illustrate that the proposed algorithm is feasible and efficient, as well as easy to be implemented.