HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network

Chang, Yi; Yan, Luxin; Fang, Houzhang; Zhong, Sheng; Liao, Wenshan

doi:10.1109/tgrs.2018.2859203

Cited by 244 publications

(107 citation statements)

References 55 publications

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“…In our unified spatial-spectral paradigm, the usage of WNNM [21] is not a must. In future, we plan to adopt Convolutional Neural Network [8,47,45] to explore non-local similarity; and automated machine learning [42] to help tuning and configuring hyper-parameters. 6.…”

Section: Resultsmentioning

confidence: 99%

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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)

148

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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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Section: Resultsmentioning

confidence: 99%

“…Remark 3.2. While there are many other spatial denoising methods, e.g., TV [25], wavelets [11,32] and CNN [8], can be used, in this paper, we use WNNM [21] to denoise each patch group tensor, as it is widely used and gives state-ofthe-art denoising performance.…”

Section: Spatial Denoising Via Mmentioning

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)

148

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“…Owing to the iterative order of S prior to A, an initial value of A 0 can be determined by successive projection algorithm [43,44]. To solve these subproblems (11) and (12), the alternating direction method of multipliers (ADMM) [45] is utilized to design an efficient solver in the following subsections.…”

Section: Jsmv-cnmf Algorithm Via Aomentioning

confidence: 99%

“…Similar to the above process of abundance estimation, we reformulate (12) in such a vector-based form that the primal variable can also be divided into several separable elements with equality constraints. To be exact, we rewrite (12) where…”

Section: Endmember Estimation Via Admmmentioning

confidence: 99%

“…However, these methods do not fully exploit the intrinsic relationship between the HSI and MSI, which often results in serious spectral distortion and performance degradation. In addition, with the development of artificial intelligence, there are many fusion algorithms based on deep neural networks [11][12][13]. For example, Yang et al [11] proposed a fusion method based on a deep convolutional neural network (CNN) with two branches for HSI and MSI separately, by exploiting spectral correlation.…”

Section: Introductionmentioning

confidence: 99%

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Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution

Yang

Ping

et al. 2019

Applied Sciences

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The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise performance. Moreover, from the perspective of matrix decomposition, the matrixing of remotely-sensed cubic data results in the loss of data’s structural information, which causes the performance degradation of reconstructed images. In addition to three-dimensional tensor-based fusion methods, Craig’s minimum-volume belief in hyperspectral unmixing can also be utilized to restore the data structure information for hyperspectral image super-resolution. To address the above difficulties simultaneously, this article incorporates the regularization of joint spatial-spectral smoothing in a minimum-volume simplex, and spatial sparsity—into the original CNMF, to redefine a bi-convex problem. After the convexification of the regularizers, the alternating optimization is utilized to decouple the regularized problem into two convex subproblems, which are then reformulated by separately vectorizing the variables via vector-matrix operators. The alternating direction method of multipliers is employed to split the variables and yield the closed-form solutions. In addition, in order to solve the bottleneck of high computational burden, especially when the size of the problem is large, complexity reduction is conducted to simplify the solutions with constructed matrices and tensor operators. Experimental results illustrate that the proposed algorithm outperforms state-of-the-art fusion methods, which verifies the validity of the new fusion approach in this article.

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