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Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning
Zhiyu Zhu1; Junhui Hou1; Jie Chen2; Huanqiang Zeng3; Jiantao Zhou4
2020-12-17
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
Volume30Pages:1423-1438
Abstract

This paper explores the problem of hyperspectral image (HSI) super-resolution that merges a low resolution HSI (LR-HSI) and a high resolution multispectral image (HR-MSI). The cross-modality distribution of the spatial and spectral information makes the problem challenging. Inspired by the classic wavelet decomposition-based image fusion, we propose a novel lightweight deep neural network-based framework, namely progressive zero-centric residual network (PZRes-Net), to address this problem efficiently and effectively. Specifically, PZRes-Net learns a high resolution and zero-centric residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension. And the resulting residual image is then superimposed onto the up-sampled LR-HSI in a meanvalue invariant manner, leading to a coarse HR-HSI, which is further refined by exploring the coherence across all spectral bands simultaneously. To learn the residual image efficiently and effectively, we employ spectral-spatial separable convolution with dense connections. In addition, we propose zero-mean normalization implemented on the feature maps of each layer to realize the zero-mean characteristic of the residual image. Extensive experiments over both real and synthetic benchmark datasets demonstrate that our PZRes-Net outperforms stateof-the-art methods to a significant extent in terms of both 4 quantitative metrics and visual quality, e.g., our PZRes-Net improves the PSNR more than 3dB, while saving 2.3× parameters and consuming 15× less FLOPs. The code is publicly available at https://github.com/zbzhzhy/PZRes-Net.

KeywordHyperspectral Imagery Super-resolution Image Fusion Deep Learning Zero-mean Normalization Cross-modality
DOI10.1109/TIP.2020.3044214
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000604831700004
Scopus ID2-s2.0-85099025989
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Cited Times [WOS]:12   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorJunhui Hou
Affiliation1.Department of Computer Science, City University of Hong Kong, Hong Kong
2.Department of Computer Science, Hong Kong Baptist University, Hong Kong
3.School of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
4.Department of Computer and Information Science, University of Macau, Macau
Recommended Citation
GB/T 7714
Zhiyu Zhu,Junhui Hou,Jie Chen,et al. Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning[J]. IEEE Transactions on Image Processing,2020,30:1423-1438.
APA Zhiyu Zhu,Junhui Hou,Jie Chen,Huanqiang Zeng,&Jiantao Zhou.(2020).Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning.IEEE Transactions on Image Processing,30,1423-1438.
MLA Zhiyu Zhu,et al."Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning".IEEE Transactions on Image Processing 30(2020):1423-1438.
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