Generalized nuclear norm and Laplacian scale mixture based low-rank and sparse decomposition for video foreground-background separation

Yang, Zhenzhen; Lü, Fan; Yang, Yongpeng; Yang, Zhen; Gui, Guan

doi:10.1016/j.sigpro.2020.107527

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…F is the Frobenius norm. When τ 2 → ∞, the model ( 15) degenerates into the model (3). Model ( 15) can be transformed into a constraint problem as shown in equation ( 16):…”

Section: B the Robust Generalized Nuclear Norm And Structured Sparse Norm (Rgnnssn) Methodsmentioning

confidence: 99%

“…< +∞, and then any accumulation point (B * , F * , Y * ) of the sequences B k , F k , Y k is a stationary point of problem (3).…”

Section: Convergence Analysis For the Proposed Algorithmsmentioning

confidence: 99%

“…The technology of video foreground-background separation [1]- [3] which extracts the key information from video data is a core step in video processing. It plays an important role in traffic control, social security, signal processing.…”

Section: Introductionmentioning

confidence: 99%

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Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Yang

et al. 2020

IEEE Access

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Low-rank and sparse decomposition (LRSD) has attracted wide attention in video foregroundbackground separation and many other fields. However, the traditional LRSD methods have many tough problems, such as the problems of the low accuracy of the surrogate functions of rank and sparsity, ignoring the spatial information of the videos and sensitivity to noise, etc. To deal with these problems, this paper proposes the generalized nuclear norm and structured sparse norm (GNNSSN) method based LRSD for video foreground-background separation, which introduces the generalized nuclear norm (GNN) and the structured sparse norm (SSN) to approximate the rank function and the l 0 -norm of the LRSD method. In addition, we extend our proposed model to a robust model against noise for practical applications, and we called the extended method as the robust generalized nuclear norm and structured sparse norm (RGNNSSN) method. At last, we use the alternating direction method of multipliers (ADMM) to solve our proposed two methods. Experimental results and discussions on video foreground-background separation demonstrate that our proposed two methods have better performances than other LRSD based foreground-background separation methods.INDEX TERMS Low-rank and sparse decomposition, generalized nuclear norm, structured sparse norm, alternating direction method of multipliers, foreground-background separation.

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“…F is the Frobenius norm. When τ 2 → ∞, the model ( 15) degenerates into the model (3). Model ( 15) can be transformed into a constraint problem as shown in equation ( 16):…”

Section: B the Robust Generalized Nuclear Norm And Structured Sparse Norm (Rgnnssn) Methodsmentioning

confidence: 99%

“…< +∞, and then any accumulation point (B * , F * , Y * ) of the sequences B k , F k , Y k is a stationary point of problem (3).…”

Section: Convergence Analysis For the Proposed Algorithmsmentioning

confidence: 99%

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Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Yang

et al. 2020

IEEE Access

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“…For instance, a reflection image can be regarded as the superimposition of a reflection source and the background source and, for the problem of dehazing removal, a ground source is superimposed onto a haze source. Therefore, blind image separation (BIS) techniques are suitable for solving a variety of similar image processing problems and play an important role in image processing tasks [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Blind Image Separation Method Based on Cascade Generative Adversarial Networks

Jia

Sun

et al. 2021

Applied Sciences

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To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.

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“…Signal reconstruction technologies have attracted much attention in the fields of channel estimation, image recovery, sparse unknown system identification [1][2][3][4][5][6]. In many cases, sparse unknown systems have only a few nonzero entries, and these limited nonzero or large coefficients response appear independently in different locations over a long pulse.…”

Section: Introductionmentioning

confidence: 99%

Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

et al. 2020

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In this work, an effective adaptive block-sparsity log-sum least mean square (BSLS-LMS) algorithm is proposed to improve the convergence performance of cluster sparse system identification. The main idea of the proposed scheme is to add a new block-sparsity induced term into the cost function of the LMS algorithm. We utilize the 1 norm of adaptive tap weights and log-sum as a mixed constraint, via optimizing the cost function through the gradient descent method, the proposed adaptive filtering method can iteratively move the identified signals towards the optimal solutions, and finally identify the unknown system accurately. The cluster-sparse system response, with block length and arbitrary average sparsity, is generated by a Markov-Gaussian (M-G) model. For the white Gaussian input data, the theoretical formulas of the steady-state mis-adjustment and convergence behaviors of the BSLS-LMS are derived in a general sparse system and a block sparse system, respectively. Numerical experiments demonstrate that the proposed adaptive BSLS-LMS algorithm achieves much better convergence behavior than conventional sparse signal recovery solutions. The experimental study also verifies the consistency between the simulation results and the theoretical analysis.

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Generalized nuclear norm and Laplacian scale mixture based low-rank and sparse decomposition for video foreground-background separation

Cited by 20 publications

References 27 publications

Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Blind Image Separation Method Based on Cascade Generative Adversarial Networks

Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

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