Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements

Cao, Wenfei; Wang, Yao; Sun, Jian; Meng, Deyu; Yang, Can; Cichocki, Andrzej; Zhao, Xu

doi:10.1109/tip.2016.2579262

Cited by 156 publications

(75 citation statements)

References 54 publications

(84 reference statements)

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“…Visual results of GPD-IALM, GPD-DECOLOR, GPD-NRA, and GPD-LRSTV on HVS-aa12 sequence. First column is the false color images of frame (11,12,14,16,18,20,22,24,26,28,30), respectively. Second column is the ground truth.…”

Section: Resultsmentioning

confidence: 99%

“…Although the combination of the TV regularization and lowrank decomposition has been exploited in conventional video object detection [29][30][31], it is the first time to use the low-rank decomposition based method in hyperspectral video. The advantage of hyperspectral video is the abundant spectral information that can provide us more information about the gas plume.…”

mentioning

confidence: 99%

“…In addition, both the sparse and TV constraints are imposed on the gas plume part according to the characteristic of gas plume. However, different regularization combinations are imposed on the foreground since the moving objects in [30,31] are different from the gas plume.…”

mentioning

confidence: 99%

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Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Chanussot

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-Hyperspectral video sequences (HVSs) are well suited for gas plume detection. The high spectral resolution allows the detection of chemical clouds even when they are optically thin. Processing this new type of video sequences is challenging and requires advanced image and video analysis algorithms. In this paper, we propose a novel method for gas plume detection recorded in HVSs. Based on the assumption that the background is stationary and the gas plume is moving, the proposed method separates the background from the gas plume via a low-rank and sparse decomposition. Furthermore, taking into consideration that the gas plume is continuous in both spatial and temporal dimensions, we include total variation regularization in the constrained minimization problem, which we solve using the augmented Lagrangian multiplier method. After applying the above process to each extracted feature, a novel fusion strategy is proposed to combine the information into a final detection result. Experimental results using real data sets indicate that the proposed method achieves very promising gas plume detection performance.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Chanussot

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…Motivated by [33], separating X into a set of image patches Ω = {X n ∈ R b×b×B } P p=1 (where b is the patch size, P is the number of 3D patches with overlap), and by performing block matching [34], a group of patches that is most similar to each patch X p can be extracted. By stacking all these patches together, we can get a clustered 4th-order tensor T k with size b × b × B × d, where d is the number of 3D patches in the kth cluster.…”

Section: Nonlocal Low-rank Tensor Approximationmentioning

confidence: 99%

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.

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“…static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g. dynamic perfusion enhanced information, which is approximately sparse over time [20], [21]. In the T-RPCA model, the tensor-based decomposition (i.e., Tucker decomposition [22] and CANDE-COMP/PARAFAC (CP) decomposition [23]) operator is utilized to describe “background” part of DCPCT image and the tensor total variation (TTV) is utilized to regularize the dynamic perfusion information in the DCPCT image.…”

Section: Introductionmentioning

confidence: 99%

Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization

Zeng

Xie

Cao

et al. 2017

IEEE Trans. Med. Imaging

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Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the “background” part with spatial–temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial–temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the “background” in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.

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Total Variation Regularized Tensor RPCA for Background Subtraction From Compressive Measurements

Cited by 156 publications

References 54 publications

Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization

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