Nonconvex Low Rank Approximation With Phase Congruency Regularization for Mixed Noise Removal

Abstract

Mixed noise removal from a natural image is a challenging task since the complex noise distribution usually is inestimable. Many noise removal methods based on the low rank approximation have an excellent image denoising performance and are effective for recovering the images corrupted by Gaussian noise. These methods based on the additive white Gaussian noise(AWGN) model are sensitive to the outliers and non-Gaussian noise, such as the salt-and-pepper impulse noise (SPIN) and random valued impulse noise (RVIN). Such methods for mixed noise removal, however, are less effective in preserving image structures and tend to undesired staircase artifacts. This paper presents a novel Nonconvex Low Rank Model with Phase congruency and overlapping Group sparsity regularization (NLRM-PG) for mixed noise removal. Moreover, an efficient optimization algorithm under the alternating direction method of multipliers and majorization minimization framework is proposed to solve the NLRM-PG model. We demonstrate that the proposed method is effective for preserving local irregular structures and it reduces staircase artifacts with the two types of mixture noise, namely, AWGN+SPIN and AWGN+RVIN. Both qualitative and quantitative experiment results on synthetic noisy images and real noisy images illustrate that the proposed method can remove mixture noise in images more efficiently than the existing methods can do. And the results also outperform those obtained by using the competing state-of-the-art methods, particularly for the removal of high-density impulse noise.