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research-article

Super-resolution of hyperspectral image via superpixel-based sparse representation

Authors:

Shutao LiAuthors Info & Claims

Neurocomputing, Volume 273, Issue C

Pages 171 - 177

https://doi.org/10.1016/j.neucom.2017.08.019

Published: 17 January 2018 Publication History

Abstract

In this paper, a novel superpixel-based sparse representation (SSR) model is proposed for hyperspectral image (HSI) super-resolution. Specifically, given a HSI with low spatial resolution and a multispectral image (MSI) with high spatial resolution, the proposed SSR approach first learns a spectral dictionary from HSI and constructs a transformed dictionary corresponding to MSI. Then, the SSR method clusters the MSI into superpixels, whose shape and size can be adaptively adjusted according to the local structures. Since pixels within each superpixel have strong similarities, the SSR method simultaneously decomposes them on the transformed dictionary to generate the corresponding fractional abundance coefficient matrix, which can exploit the similarities within the superpixel to improve the sparse decomposition. Finally, the high resolution hyperspectral image can be reconstructed with the obtained fractional abundance coefficient matrix. Experimental results show that the proposed approach is superior to some well-known HSI super-resolution methods.

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Cited By

Zheng WXie Z(2023)Spatial-Spectral Deep Residual Network for Hyperspectral Image Super-ResolutionSN Computer Science10.1007/s42979-023-01868-04:4Online publication date: 2-Jun-2023
https://dl.acm.org/doi/10.1007/s42979-023-01868-0
Al-Obeidat FRocha ÁKhan MMaqbool FRazzaq S(2022)Parallel tensor factorization for relational learningNeural Computing and Applications10.1007/s00521-021-05692-634:11(8455-8464)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00521-021-05692-6
Yang XLiu LZhu CGuo YZhou D(2022)An improved anchor neighborhood regression SR method based on low-rank constraintThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-02022-038:2(405-418)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-020-02022-0
Show More Cited By

Super-resolution of hyperspectral image via superpixel-based sparse representation
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation

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Published In

cover image Neurocomputing

Neurocomputing Volume 273, Issue C

January 2018

567 pages

ISSN:0925-2312

Issue’s Table of Contents

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 17 January 2018

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Cited By

Zheng WXie Z(2023)Spatial-Spectral Deep Residual Network for Hyperspectral Image Super-ResolutionSN Computer Science10.1007/s42979-023-01868-04:4Online publication date: 2-Jun-2023
https://dl.acm.org/doi/10.1007/s42979-023-01868-0
Al-Obeidat FRocha ÁKhan MMaqbool FRazzaq S(2022)Parallel tensor factorization for relational learningNeural Computing and Applications10.1007/s00521-021-05692-634:11(8455-8464)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00521-021-05692-6
Yang XLiu LZhu CGuo YZhou D(2022)An improved anchor neighborhood regression SR method based on low-rank constraintThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-02022-038:2(405-418)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00371-020-02022-0
Wang QChen Z(2022)A Deep Wavelet Network for High-Resolution Microscopy Hyperspectral Image ReconstructionComputer Vision – ECCV 2022 Workshops10.1007/978-3-031-25082-8_44(648-662)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-25082-8_44
Sarkar SSahay R(2021)A Non-Local Superpatch-Based Algorithm Exploiting Low Rank Prior for Restoration of Hyperspectral ImagesIEEE Transactions on Image Processing10.1109/TIP.2021.309378030(6335-6348)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2021.3093780
Zhu ZHou JChen JZeng HZhou J(2020)Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual LearningIEEE Transactions on Image Processing10.1109/TIP.2020.304421430(1423-1438)Online publication date: 29-Dec-2020
https://dl.acm.org/doi/10.1109/TIP.2020.3044214
Cai YGeng ZLiang YFu P(2020)Extraction of Spectral-Spatial 3-Dimensional Homogeneous Regions from Hyperspectral Images and Its Application to Fast ClassificationPattern Recognition and Computer Vision10.1007/978-3-030-60639-8_17(200-207)Online publication date: 16-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-60639-8_17
Hua ZZhang HLi J(2019)Image Super Resolution Using Fractal Coding and Residual NetworkComplexity10.1155/2019/94191072019Online publication date: 28-Nov-2019
https://dl.acm.org/doi/10.1155/2019/9419107
Shi ZChen CXiong ZLiu DZha ZWu F(2018)Deep Residual Attention Network for Spectral Image Super-ResolutionComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11021-5_14(214-229)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11021-5_14

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