Multi-band Weighted $l_p$ Norm Minimization for Image Denoising
This work addresses a specific bottleneck in image denoising for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of low rank matrix approximation over-shrinking rank components uniformly by proposing multi-band weighted l_p norm minimization (MBWPNM), which achieves better performance in image denoising compared to state-of-the-art algorithms, as demonstrated in experiments on additive white Gaussian noise and realistic noise removal.
Low rank matrix approximation (LRMA) has drawn increasing attention in recent years, due to its wide range of applications in computer vision and machine learning. However, LRMA, achieved by nuclear norm minimization (NNM), tends to over-shrink the rank components with the same threshold and ignore the differences between rank components. To address this problem, we propose a flexible and precise model named multi-band weighted $l_p$ norm minimization (MBWPNM). The proposed MBWPNM not only gives more accurate approximation with a Schatten $p$-norm, but also considers the prior knowledge where different rank components have different importance. We analyze the solution of MBWPNM and prove that MBWPNM is equivalent to a non-convex $l_p$ norm subproblems under certain weight condition, whose global optimum can be solved by a generalized soft-thresholding algorithm. We then adopt the MBWPNM algorithm to color and multispectral image denoising. Extensive experiments on additive white Gaussian noise removal and realistic noise removal demonstrate that the proposed MBWPNM achieves a better performance than several state-of-art algorithms.