Group sparsity based regularization model for remote sensing image stripe noise removal

Chen, Yong; Huang, Ting‐Zhu; Deng, Liang-Jian; Zhao, Xi-Le; Wang, Min

doi:10.1016/j.neucom.2017.05.018

Cited by 72 publications

(54 citation statements)

References 36 publications

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“…In this section, we conduct experiments on both simulated and real data to test the efficiency of the proposed method. Five state-of-the-art destriping methods were chosen for comparison: the filtering-based method [12] (WAFT), the statistics-based method [13] (SLD), and optimization-based methods such as the weighted UTV-based method [29] (WDSUV), the low-rank decomposition-based method [38] (LRSID), and the group sparsity regularization-based method [40] (GSUTV). In the simulated experiments, the original MODIS Image Band 32 of size 400 × 400, which is available at https:// ladsweb.nascom.nasa.gov/, and the IKONOS image of size 377 × 331, which can be downloaded from https://openremotesensing.net/, were used by adding synthetic stripe noises to the clean image.…”

Section: Methodsmentioning

confidence: 99%

“…After estimating the stripe S from (2), the ground-truth image U is obtained by F − S. We explain in detail the motivation of each term in our model. To illustrate the stripe properties appropriately, we use GSUTV [40], which achieves the destriping by estimating the stripe component, to remove stripes in the Terra MODIS image and explore the stripe properties in results. Figure 1a-c presents the degraded image, the destriping result, and the stripe component, respectively.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…As shown in Figure 1c, the stripe component has a special column structure. As Chen et al [40] pointed out, the stripe component is composed by column vectors, and each column can be considered as a group. Moreover, we observe that the structure of the stripe component exhibited joint sparsity in the sense that the non-zeros of S 1: , • • • , S m: absolutely appeared in the same position.…”

Section: The Proposed Modelmentioning

confidence: 99%

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Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Sun

Huang

et al. 2019

Remote Sensing

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Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

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Section: Methodsmentioning

confidence: 99%

Section: The Proposed Modelmentioning

confidence: 99%

Section: The Proposed Modelmentioning

confidence: 99%

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Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Sun

Huang

et al. 2019

Remote Sensing

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“…Considering the characteristic of stripes, sparse representation and sparsity promoting priors have been utilized to remove the stripes in HSIs [15,16,32,33]. However, this sparsity characteristic has not been considered under a tensor framework.…”

Section: Introductionmentioning

confidence: 99%

Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

et al. 2020

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This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness along the stripe direction. Stripe-free hyperspectral images are smooth in spatial domain, with strong spectral correlation. Existing destriping approaches often do not fully investigate such intrinsic characteristics of the stripes in spatial and spectral domains simultaneously. Those methods may generate new artifacts in extreme areas, causing spectral distortion. The proposed GLTSA model applies two ℓ 0 -norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two ℓ 1 -norm regularizers are applied to the gradients of clean image in spatial and spectral domains. The double non-convex functions in GLTSA are converted to single non-convex function by mathematical program with equilibrium constraints (MPEC). Experiment results demonstrate that GLTSA is effective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures.

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“…In [22], the UV model was converted to a least square problem by an iteratively reweighted technique that was easy to implement. Regarding the destriping problem as an ill-posed inverse problem, the (column) sparsity property and low rank property of the stripe noise served as a regularization to improve the stripe estimation performance in [23][24][25]. Chen et al [26] combined the group sparsity constraint and total variation regularization to remove the stripe noise and preserve edge information.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

et al. 2018

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Stripe noise removal continues to be an active field of research for remote image processing. Most existing approaches are prone to generating artifacts in extreme areas and removing the stripe-like details. In this paper, a weighted double sparsity unidirectional variation (WDSUV) model is constructed to reduce this phenomenon. The WDSUV takes advantage of both the spatial domain and the gradient domain's sparse property of stripe noise, and processes the heavy stripe area, extreme area and regular noise corrupted areas using different strategies. The proposed model consists of two variation terms and two sparsity terms that can well exploit the intrinsic properties of stripe noise. Then, the alternating direction method of multipliers (ADMM) optimal solver is employed to solve the optimization model in an alternating minimization scheme. Compared with the state-of-the-art approaches, the experimental results on both the synthetic and real remote sensing data demonstrate that the proposed model has a better destriping performance in terms of the preservation of small details, stripe noise estimation and in the mean time for artifacts' reduction.

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Group sparsity based regularization model for remote sensing image stripe noise removal

Cited by 72 publications

References 36 publications

Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

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