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.INDEX TERMS Mixed noise removal, nonconvex low rank approximation, overlapping group sparsity, phase congruency.

A Two-Stage Algorithm for the Detection and Removal of Random-Valued Impulse Noise Based on Local Similarity

“…θ c = 0.31σ 3 + 0.63σ 2 + 0.52σ + 0.03 (16) where θ f and θ c are the LS detection thresholds of pixels in the flat area and the complex area, respectively. σ is the noise level of the corrupted image, which was estimated from Formula (12)- (14). Then the impulse noise detector based on LS can be designed as follows:…”

“…Step 3: Estimate the overall noise level of the original noisy image through (12)- (14) and then obtain the best LS detection thresholds for pixels in the flat area and the complex area through formulas (15) and (16).…”

CircWaveDL: Modeling of Optical Coherence Tomography Images Based on a new Supervised Tensor-based Dictionary Learning for Classification of Macular abnormalities

“…To tackle this issue, tensor-based dictionary learning approaches have been developed, which preserve the multidimensional nature of data during image processing, thereby eliminating the need to vectorize it [4], [15], [23]- [25]. In these approaches, various techniques including CANDECOMP/PARAFAC (CP) [26]- [31] and Tucker [24], [25], [31], [32] decomposition models, have been effectively employed, to decompose a high-order tensor into lower-dimensional components [31]. The Tucker decomposition is a higher-order generalization of PCA [31], while CP can be perceived as a O higher-order extension of the matrix singular value decomposition (SVD), where a tensor is expressed by a limited number of rank-one components [33].…”

“…In these approaches, various techniques including CANDECOMP/PARAFAC (CP) [26]- [31] and Tucker [24], [25], [31], [32] decomposition models, have been effectively employed, to decompose a high-order tensor into lower-dimensional components [31]. The Tucker decomposition is a higher-order generalization of PCA [31], while CP can be perceived as a O higher-order extension of the matrix singular value decomposition (SVD), where a tensor is expressed by a limited number of rank-one components [33]. Although the Tucker model is frequently used in data compression tasks, the CP model can be a preferable choice in certain situations, since it is entirely distinct from the Tucker model and remains essentially unique under relatively mild conditions.…”

Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

“…However, it is difficult to separate the two kinds of noises completely in practical application. e high-frequency information of the target in the image is often lost, resulting in blurring of edges and textures [6], so it is necessary to explore a better denoising method.…”