|Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning|
|Zhu，Zhiyu1; Hou，Junhui2; Chen，Jie3; Zeng，Huanqiang4; Zhou，Jiantao5|
|Source Publication||IEEE Transactions on Image Processing|
|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 mean-value 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 state-of-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.|
|Keyword||cross-modality deep learning Hyperspectral imagery Hyperspectral imaging image fusion Image reconstruction Optimization Principal component analysis Residual neural networks Spatial resolution super-resolution Tensors zero-mean normalization|
|URL||View the original|
|Document Type||Journal article|
|Collection||University of Macau|
|Affiliation||1.Department of Computer Science, City University of Hong Kong, Hong Kong.|
2.Department of Computer Science, City University of Hong Kong, Hong Kong. (e-mail: firstname.lastname@example.org)
3.Department of Computer Science, Hong Kong Baptist University, Hong Kong.
4.School of Information Science and Engineering, Huaqiao University, Xiamen 361021, China.
5.Department of Computer and Information Science, University of Macau, Macau.
|Zhu，Zhiyu,Hou，Junhui,Chen，Jie,et al. Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning[J]. IEEE Transactions on Image Processing,2020.|
|APA||Zhu，Zhiyu,Hou，Junhui,Chen，Jie,Zeng，Huanqiang,&Zhou，Jiantao.(2020).Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning.IEEE Transactions on Image Processing.|
|MLA||Zhu，Zhiyu,et al."Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning".IEEE Transactions on Image Processing (2020).|
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