Image restoration using total variation with overlapping group sparsity

Liu, Jun; Huang, Ting-Zhu; Selesnick, Ivan; Lv, Xiao-Guang; Chen, Po‐Yu

doi:10.1016/j.ins.2014.10.041

Cited by 157 publications

(98 citation statements)

References 59 publications

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“…The symmetric alternating direction method with multipliers (symmetric ADMM) is an acceleration method of ADMM, which can be used to solve the constraint optimization formulation in image processing [34][35][36][37][38][39][40][41][42][43].…”

Section: Symmetric Admmmentioning

confidence: 99%

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Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

et al. 2018

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Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images. Plenty of sparse unmixing methods have been proposed in the literature that achieved promising performance. However, many of these methods overlook the latent geometrical structure of the hyperspectral data which limit their performance to some extent. To address this issue, a double reweighted sparse and graph regularized unmixing method is proposed in this paper. Specifically, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances. In this way, the latent geometrical structure of the hyperspectral data can be transferred to the abundance space. In addition, a double weighted sparse regularizer is used to enhance the sparsity of endmembers and the fractional abundance maps, where one weight is introduced to promote the sparsity of endmembers as a hyperspectral image typically contains fewer endmembers compared to the overcomplete spectral library and the other weight is exploited to improve the sparsity of the abundance matrix. The weights of the double weighted sparse regularizer used for the next iteration are adaptively computed from the current abundance matrix. The experimental results on synthetic and real hyperspectral data demonstrate the superiority of our method compared with some state-of-the-art approaches.

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Section: Symmetric Admmmentioning

confidence: 99%

“…Many optimization methods can be applied to solve the proposed formulation (P2), such as the split Bregman method, alternating direction method with multipliers [35][36][37][38][39][40]42,43,49,50]. Here, we employ symmetric ADMM to solve (P2) due to its simplicity and efficiency [34].…”

Section: Optimization Algorithmmentioning

confidence: 99%

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

et al. 2018

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“…In these approaches, many parameters have to be chosen and causes time consuming. To overcome this drawback, an alternating direction minimization method of multipliers (ADMM) has been widely used in recent image-processing tasks [14,15]. Its outstanding performance is that there is no need to resolve the subproblems and no inner iterations.…”

Section: Related Workmentioning

confidence: 99%

“…And it is natural to extend the overlapping group sparsity prior to solve the two-dimension problem such as image restoration. It has been used as a penalty term for TV models and proven to be effective for alleviating staircase effect [15].…”

Section: Overlapping Sparsity Priormentioning

confidence: 99%

A novel local and nonlocal total variation combination method for image restoration in wireless sensor networks

Shi

Liang²

2017

J Wireless Com Network

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In this paper, we propose a novel local and nonlocal total variation combination method for image restoration in wireless sensor networks (WSN), which plays an important role in improving the quality of the transmitted image. First, the degrade image is preprocessed by an image smoothing scheme to divide the image into two regions. One contains edges and flat regions by the local TV term. The other is rich in image details and regularized by the nonlocal TV term. Then, the alternating direction method of multipliers (ADMM) algorithm is adopted to optimize the complex object function, and two key parameters are discussed for better performance. Finally, we compare our method with several recent state-of-the-art methods and illustrate the efficiency and performance of the proposed model by experimental results in peak signal to noise ratio (PSNR) and computing time.

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“…Filtering-based methods suppress the stripe noise by constructing a filter on a transformed domain, such as Fourier transform [1,13], wavelet analysis [14,15], TV and group sparsity regularization are used to depict the prior of stripe component. Since the TV regularization is a very popular approach in image processing because of its effectiveness in preserving edge information and the spatial piecewise smoothness [2,36,37], we employ TV regularization to describe the image component prior. Finally, we establish an image decomposition framework based optimization model to remove stripe noise, which jointly combines image component prior and stripe component prior.…”

Section: Introductionmentioning

confidence: 99%

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

et al. 2017

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Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.

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Image restoration using total variation with overlapping group sparsity

Cited by 157 publications

References 59 publications

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

A novel local and nonlocal total variation combination method for image restoration in wireless sensor networks

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

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