Reweighted Low-Rank Matrix Recovery and its Application in Image Restoration

Peng, Yigang; Suo, Jinli; Dai, Qionghai; Wang, Xu

doi:10.1109/tcyb.2014.2307854

Cited by 110 publications

(53 citation statements)

References 30 publications

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“…In [67], Candès proposed a reweighted l 1 minimization to address the imbalance in which larger coefficients are penalized more heavily than smaller ones. Subsequently, the reweighted scheme achieved great success in many publications [68][69][70]. As indicated in Table 1, the computing time of optimizing methods is always a major concern.…”

Section: Model Constructionmentioning

confidence: 99%

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019

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Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

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Section: Model Constructionmentioning

confidence: 99%

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019

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“…By minimizing a sequence of weighted 1 norm, a significant performance improvement is obtained on sparse recovery. Inspired by it, many improved RPCA models have been proposed [39], [40], [41], [42]. Motivated by these state-of-the-art models, we adopt a similar reweighted scheme for the values in the target patch-tensor.…”

Section: A Reweighted Infrared Patch-tensor Modelmentioning

confidence: 99%

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Dai

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

320

169

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Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise localstructure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.Index Terms-infrared small target detection, infrared patchtensor model, reweighted higher-order robust principal component analysis, non-local self-correlation prior, local structure prior. Y. Dai is with the College

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“…Inspired by the spectral correlation of HSIs, various methods based on low-rank matrix factorization (LRMF) have been proposed in the past few years. For example, Zhang et al [20] used a rank-constrained RPCA [21] while Wu et al [22] and Xie et al [23] employed weighted nuclear norm minimization (WNNM) [24,25] to enhance the restoration quality. In addition, considering the local similarity within a patch and the non-local similarity across the patches in a group simultaneously, a novel group low-rank representation (GLRR) model was proposed in [26].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Restoration under Complex Multi-Band Noises

Yue

Meng

Sun³

et al. 2018

Remote Sensing

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Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-level hierarchical Dirichlet process (HDP) to model the HSI noise structure, the noise of each band is depicted by a Dirichlet process Gaussian mixture model (DP-GMM), in which its complexity can be flexibly adapted in an automatic manner. Besides, the DP-GMM of each band comes from a higher level DP-GMM that relates the noise of different bands. The variational Bayes algorithm is also designed to solve this model, and closed-form updating equations for all involved parameters are deduced. The experiment indicates that, in terms of the mean peak signal-to-noise ratio (MPSNR), the proposed method is on average 1 dB higher compared with the existing state-of-the-art methods, as well as performing better in terms of the mean structural similarity index (MSSIM) and Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS).

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Reweighted Low-Rank Matrix Recovery and its Application in Image Restoration

Cited by 110 publications

References 30 publications

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Hyperspectral Image Restoration under Complex Multi-Band Noises

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