Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization

Yang, Jing‐Hua; Zhao, Xi-Le; Ma, Tianhui; Chen, Yong; Huang, Ting‐Zhu; Ding, Meng

doi:10.1016/j.cam.2019.06.004

Cited by 102 publications

(46 citation statements)

References 43 publications

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“…Spatial methods for destriping can be roughly divided into 1D-filtering-based methods [11,16,17], statistics-based methods [12][13][14][18][19][20], optimization-based methods [21][22][23][24][25][26][27][28][29][30], and deep-learning-based methods [31][32][33][34]. Currently, 1D-filtering-based methods are mainly performed on conventional TIR images, and their basic idea is to utilize 1D edge-preserving filters to progressively separate stripe noise from the contaminated image in horizontal and vertical directions.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, many UTV variants are developed to further enhance the model's adaptivity [22][23][24][25]. Other prior knowledge of stripes including low rank [26,27] and sparsity [28][29][30] are primely explored and applied in the relevant models as well. From a pragmatic perspective, these sophisticated optimization approaches tend to be multi-parametric and time-consuming, which makes them have lower utility.…”

Section: Related Workmentioning

confidence: 99%

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Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

2020

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Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

2020

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“…The proposed model can be solved by exploiting the ADMM framework [46], [47], [48], [49], which separates the Frobenius norm and 2,1 norm into two independent subproblems that have closed-form and iterative solutions, respectively. In our model, we can rewrite (9) as an equivalent constrained problem through introducing the auxiliary variable W = X − P, namely, we have that…”

Section: The Proposed Algorithmmentioning

confidence: 99%

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

Huang

Deng

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pansharpening is a very debated spatio-spectral fusion problem. It refers to the fusion of a high spatial resolution panchromatic (PAN) image with a lower spatial but higher spectral resolution multispectral (LRMS) image in order to obtain an image with high resolution in both the domains. In this paper, we propose a novel variational optimizationbased (VO) approach to address this issue incorporating the outcome of a deep convolutional neural network (DCNN). This solution can take advantages of both the paradigms. On one hand, higher performance can be expected introducing machine learning methods based on the training by examples philosophy into VO approaches. On other hand, the combination of VO techniques with DCNNs can aid the generalization ability of these latter. In particular, we formulate a 2-based proximal deep injection term to evaluate the distance between the DCNN outcome and the desired high spatial resolution multispectral image. This represents the regularization term for our VO model. Furthermore, a new data fitting term measuring the spatial fidelity is proposed. Finally, the proposed convex VO problem is efficiently solved by exploiting the framework of the alternating direction method of multipliers, thus guaranteeing the convergence of the algorithm. Extensive experiments both on simulated and real datasets demonstrate that the proposed approach can outperform state-of-the-art spatio-spectral fusion methods, even showing a significant generalization ability. Please find the project page: https://liangjiandeng.github.io/Projects Res/DMPIF 2020jstars.html.

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“…Han et al proposed a novel algorithm based on a new definition of similarity coefficient to remove PolSAR image speckle [58]. Yang et al used the Schatten 1/2-norm regularization to the remote sensing images destriping [59]. However, all of the above methods only focus on the restoration of the images degraded by blurring and additive structured noise or multiplicative structured noise separately.…”

Section: Introductionmentioning

confidence: 99%

A Convex Variational Approach for Image Deblurring With Multiplicative Structured Noise

Zeng

2020

IEEE Access

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The restoration of images corrupted by blurring and structured noise has attracted growing attention in the domains of image processing and computer vision. However, many works only focus on the restoration of the images degraded by blurring and additive structured noise or multiplicative structured noise separately. It is still a challenge and an open problem to restore degraded images with blurring and multiplicative structured noise, simultaneously. In this paper, based on the total variation (TV), the statistical property of the Gamma noise and the maximum a posteriori (MAP) estimator, we obtain a convex variational model to recover blurred images with multiplicative structured noise. Especially, to get this convex model, we reformulate the prior assumption of the images degradation model by division instead of multiplication. For solving this convex model, an effective alternating direction method of multipliers (ADMM) is employed. Numerical experiments are presented to illustrate the effectiveness and efficiency of the proposed model. INDEX TERMS Image deblurring, convex variational model, structured noise, multiplicative noise, ADMM.

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Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization

Cited by 102 publications

References 43 publications

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

A Convex Variational Approach for Image Deblurring With Multiplicative Structured Noise

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