Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration

Fu, Ying; Lam, Antony; Sato, Iwao; Sato, Yoichi

doi:10.1007/s11263-016-0921-6

Cited by 53 publications

(27 citation statements)

References 73 publications

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“…Image denoising is a test bed for the various technique. Consequently, numerous approaches for HSI denoising have been proposed [29,30,86]. The spectral correlation and nonlocal self-similarity are two kinds of intrinsic characteristic underlying a HSI.…”

Section: Hsi Denoisingmentioning

confidence: 99%

Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration

Chang

Yan

Zhao

et al. 2020

IEEE Trans. Cybern.

157

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Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such noises (random noise, HSI denoising), blurs (Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral and spatial downsample, HSI super-resolution). Previous HSI restoration methods are designed for one specific task only. Besides, most of them start from the 1-D vector or 2-D matrix models and cannot fully exploit the structurally spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this work, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which non-local similarity between spectral-spatial cubic and spectral correlation are simultaneously captured by 3-order tensors. Further, to improve the capability and flexibility, we formulate it as a weighted low-rank tensor recovery (WLRTR) model by treating the singular values differently, and study its analytical solution. We also consider the exclusive stripe noise in HSI as the gross error by extending WLRTR to robust principal component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed WLRTR models consistently outperform state-of-the-arts in typical low level vi-

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Section: Hsi Denoisingmentioning

confidence: 99%

Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration

Chang

Yan

Zhao

et al. 2020

IEEE Trans. Cybern.

157

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“…Since denoising is an ill-posed problem, proper regulations based on the HSI prior knowledge is necessary [17,38]. The mainstream of HSI denoising methods can be grouped into two categories: spatial non-local based methods and spectral low-rank based methods.…”

Section: Related Workmentioning

confidence: 99%

Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising

Wei

Yao

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

138

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Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, while their denoising performance benefits little from more spectral bands, the running time of these methods significantly increases. In this paper, we claim that the HSI lies in a global spectral low-rank subspace, and the spectral subspaces of each full band patch groups should lie in this global low-rank subspace. This motivates us to propose a unified spatialspectral paradigm for HSI denoising. As the new model is hard to optimize, An efficient algorithm motivated by alternating minimization is developed. This is done by first learning a low-dimensional orthogonal basis and the related reduced image from the noisy HSI. Then, the non-local low-rank denoising and iterative regularization are developed to refine the reduced image and orthogonal basis, respectively. Finally, the experiments on synthetic and both real datasets demonstrate the superiority against the stateof-the-art HSI denoising methods.

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“…According to this limitation, we are interested on improving the estimated sparce representation, so non-local redundancy properties in natural scenes are studied to enhance the representation model. For instance, non-local self-similarities of natural scenes have been included in approaches for super-resolution or denoising leading to state-of-theart performance (Elad and Aharon;2006;Dian, Fang and Li, 2017;Mairal et al, 2009;Fu et al, 2017). Similarly, to enhance the accuracy of IR methods based on sparsity, a centralized non-local sparce representation (CNSR) model has been proposed in (Dong et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps

Vargas

Arias

Rojas

et al. 2020

Tecnura

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Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem.Methodology: The dictionaries are learned from the estimated abundance data taking advantage of the depth correlation between abundance maps and the non-local self- similarity over the spatial domain. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm.Results: Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.Conclusions: In this work, we propose a hyperspectral and multispectral image fusion method based on a non-local centralized sparse representation on abundance maps. This model allows us to include the non-local redundancy of abundance maps in the fusion problem using spectral unmixing and improve the performance of the sparsity-based fusion approaches.

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Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration

Cited by 53 publications

References 73 publications

Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration

Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration

Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising

Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps

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