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

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

doi:10.3390/rs9060559

Cited by 62 publications

(37 citation statements)

References 52 publications

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“…However, determining the relationship between the parameters and the result is not an easy task, and may need many calculations. Here, we adopt the same strategy in [26] that tunes one parameter while others are fixed to analyse the relationship. We evaluate the PSNR values as a function of λ 1 when λ 2 and λ 3 are fixed.…”

Section: Analysis Of Parametersmentioning

confidence: 99%

“…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. Dou et al [27] proposed a directional l 0 sparse model for stripe noise removal.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Analysis Of Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…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%

“…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%

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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“…In this study, we will mainly focus on the random noise, whose elimination is still a challenge; fixed pattern noise can be removed by calibration routines or destriping approaches [10]. In many previous works, the random noise in HS images is modeled as a stochastic signal, which is purely additive and independent to an image signal.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

Sun

2017

Remote Sensing

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Accurate approximation of the signal-independent (SI) and signal-dependent (SD) mixed noise from hyperspectral (HS) images is a critical task for many image processing applications where the detection of homogeneous regions plays a key role. Most of the conventional methods empirically divide images into rectangular blocks and then select the homogeneous ones, but it might result in erroneous homogeneity detection, especially for highly textured HS images. To address this challenge, a superpixel segmentation algorithm is proposed in this paper, which can decompose a noisy HS image into patches that adhere to the local structures and hence persist in homogeneous characteristic. A novel spectral similarity measure is defined in the frequency domain to make the superpixel segmentation algorithm more robust to the mixed noise. Combined with an improved scatter-plot-based homogeneous superpixel selection and a multiple linear regression-based noise parameter calculation, our method can accurately estimate SD and SI noise variances from HS images with different noise conditions and various image complexities. We evaluate the proposed method with both synthetic and real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images. Experimental results demonstrate that the proposed noise estimation method outperforms the state-of-the-art methods.

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

Cited by 62 publications

References 52 publications

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

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

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

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