Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising

Wang, Hengyou; Cen, Yigang; He, Zhiquan; He, Zhihai; Zhao, Ruizhen; Zhang, Fengzhen

doi:10.1109/tip.2017.2781425

Cited by 56 publications

(18 citation statements)

References 38 publications

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“…The complexity of computing the k-NN patch grouping is O(Pgs 2 m), where P is the number of extracted similar patch groups, s is the search window of size s × s, m is the number of pixels in each patch. The complexity of computing singular value Recently, Wang et al [44] proposed a low-rank matrix recovery algorithm based on a re-weighted nuclear norm and total variation (SRLRMR). The experimental results show the excellent performance for denoising sparse large noise.…”

Section: E Computational Complexitymentioning

confidence: 99%

Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

Liu

2019

IEEE Access

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The nuclear norm minimization method emerged from a patch-based low-rank model leads to an excellent image denoising performance, where the non-local self-similarity over image patches is exploited. However, natural images are normally with complex and irregular image patches, which cannot be well represented using only a low-rank model, and thus most of them suffer from the over-penalty problem especially for images with lots of local irregular structures (e.g., fine details or sharp edges), and then results in over-smoothing problem after denoising. On the other hand, in order to represent the irregular components, edges defined over pixel level are often exploited. While the total variation (TV) is a well-known prior to remove noises and preserve edges, it might yield undesired staircase artifacts. The total generalized variation (TGV), a generalization of TV, can largely alleviate such staircase artifacts. Consequently, in order to deal with the over-smoothing problem aroused by a low-rank model, we propose a re-weighted TGV regularized nuclear norm minimization model for local structure preserving image denoising. Thanks to the split Bregman method, our proposed model can be effectively solved. A re-weighted strategy is developed to adaptively update the weight parameters of TGV regularization. The encouraging experimental results on noisy images demonstrate the effectiveness of our proposed method. INDEX TERMS Low rank, nuclear norm minimization, total generalized variation, image denoising, local structures preserving.

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Section: E Computational Complexitymentioning

confidence: 99%

Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

Liu

2019

IEEE Access

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“…Restoring a clean image from its noisy version is a hot research issue in the field of low-level computer vision because the noise can destroy important details in the image and reduce its quality. Image denoising is a broad research subject in the past few decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The extensively studied image denoising algorithms can be divided into two main categories: sparse representation and lowrank matrix recovery (LRMR).…”

Section: Introductionmentioning

confidence: 99%

Improved RPCA method via non‐convex regularisation for image denoising

Wang

Xia

Wang

et al. 2020

IET signal process.

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The traditional robust principal component analysis (RPCA) model is based on the nuclear norm, which usually underestimates the singular values of the low-rank matrix. As a consequence, the restoration image experiences serious interference by Gaussian noise, and the image quality degenerates during the denoising process. Therefore, an improved RPCA method via non-convex regularisation terms is proposed to remedy the above shortcomings. First, in order to estimate the singular value of the low-rank matrix more accurately, the authors employ the non-convex penalty function and add a weight vector to it. Then, the regularisation with non-convex penalty function and its weighted version are used to replace the nuclear norm and entry-wise l 1 norm in original RPCA, respectively, to establish an improved model. Finally, an optimal solution algorithm is derived by developing the alternating direction multiplier method. Experimental results show that the proposed method has better performance in terms of both quantitative measurement and visual perception quality than other several state-of-the-art image denoising methods.

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“…Recently, low-rank recovery theory [21] have been widely applied in image processing, such as object detection [22], background subtraction [23], image denoising [24]. Gao et al [9] firstly quoted the theory to single-frame detection task under the prior assumptions of target sparsity and background nonlocal correlation.…”

Section: Introductionmentioning

confidence: 99%

Graph-Regularized Laplace Approximation for Detecting Small Infrared Target Against Complex Backgrounds

Zhou

Dai

et al. 2019

IEEE Access

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Against complex background containing the tiny target, high-performance infrared small target detection is always treated as a difficult task. Many low-rank recovery-based methods have shown great potential, but they may suffer from high false or missing alarm when encountering the background with intricate interferences. In this paper, a novel graph-regularized Laplace low-rank approximation detecting model (GRLA) is developed for infrared dim target scenes. Initially, a non-convex Laplace low-rank regularizer instead of the nuclear norm is employed to boost the accuracy of heterogeneous background estimation. Then, to maintain the intrinsic structure between background patch-image, the graph regularization is incorporated in the detecting model. Besides, aiming at reducing the nontarget outliers, a reweighted l 1 norm with nonnegative constraint is used. Finally, the proposed model is extended to a generalized framework (G-GRLA) by replacing different non-convex rank functions. With the help of the alternating direction method of multiplier (ADMM), the solution of the proposed model is obtained by an iterative optimization scheme. The experimental results on extensive actual infrared images present the superior performance of our proposed method to compare with the state-of-the-art methods. INDEX TERMS Small target detection, nonconvex low-rank regularizer, manifold information, graph regularization, reweighted l 1 norm with nonnegative constraint.

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Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising

Cited by 56 publications

References 38 publications

Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

Image Denoising via Nonlocal Low Rank Approximation With Local Structure Preserving

Improved RPCA method via non‐convex regularisation for image denoising

Graph-Regularized Laplace Approximation for Detecting Small Infrared Target Against Complex Backgrounds

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