Super-resolution hyperspectral imaging with unknown blurring by low-rank and group-sparse modeling

Huang, Huijuan; Christodoulou, Anthony G.; Sun, Weidong

doi:10.1109/icip.2014.7025432

Cited by 18 publications

(10 citation statements)

References 11 publications

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“…Firstly, though the proposed NLRTATV has only tested on the experiments with Gaussian blurring kernel, which is the situation in which no prior information on the blurring operator is known, a similar idea used in [18] could be incorporated into our model for tackling HSI super-resolution tasks with unknown blurring kernels. Secondly, it could be interesting to extend the deep learning ideas of [44] and [45] to design a deep tensor architecture to learn the more intrinsic characteristics of the HSI cube, and, as a result, to provide much better super-resolution reconstruction results.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al [17] proposed a maximum a posteriori based HSI super-resolution reconstruction algorithm, in which PCA is employed to reduce computational load and simultaneously remove noise. Huang et al [18] presented a novel super-resolution approach of HSIs by joint low-rank and group-sparse modeling. Their approach can also deal with the situation that the system blurring is unknown.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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“…Low rank representation (LRR) has been used in HSI analysis [22]. Lu et al [23] introduced LRR to remove stripe noise in HSI based on correlation among different bands, and a graph regularization is considered for the local geometrical structure.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation

et al. 2019

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Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI's discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixel segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a correlation between different bands of the same signal, it is easy to know the property of low rank. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of samples, we introduce the superpixel segmentation method, which allows samples of the same type to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.

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“…However, these methods cause HSI blur, which is not conducive to practical application. In recent years, some single-image HSI SR methods based on sparse representation were proposed [14,15]. In order to exploit the self-similarity in spatial and spectral domains, a multi-dictionary sparse representation method was proposed for HSI SR [15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution

Tang

2020

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

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The spectral resolution of hyperspectral images (HSIs) is very high. Nevertheless, their spatial resolution is low due to various hardware limitations. Therefore, it is important to study HSI super-resolution to improve their spatial resolution. In this paper, for hyperspectral single-image super-resolution, we propose a multi-scale feature fusion and aggregation network with 3D convolution (MFFA-3D) by cascading the MFFA-3D block. The MFFA-3D block includes group multi-scale feature fusion part and multi-scale feature aggregation part. In group multi-scale feature fusion part, a novel group multi-scale feature fusion method is proposed. Group feature fusion module and two-step multi-scale module are proposed in multi-scale feature aggregation part. In order to prevent spectral distortion, a spectral gradient loss function is proposed and combined with the mean square error (MSE) loss function to form the final loss function. Since the proposed super-resolution network is full 3D convolutional network, our method can perform direct super-resolution transfer even if the number of the bands of test images is different from that of training images. The experiments over simulated and real hyperspectral images demonstrate the superiority of the proposed method in terms of qualitative and quantitative evaluation.

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Super-resolution hyperspectral imaging with unknown blurring by low-rank and group-sparse modeling

Cited by 18 publications

References 11 publications

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation

Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution

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