Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution-Reference-Cited by-同舟云学术

Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

Author:

Gao Lianru¹^ORCID,Li Jiaxin¹^ORCID,Zheng Ke²^ORCID,Jia Xiuping³^ORCID

Affiliation:

1. Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

2. College of Geography and Environment, Liaocheng University, Liaocheng, China

3. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Funder

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference89 articles.

1. A survey on hyperspectral image restoration: From the view of low-rank tensor approximation;liu;arXiv 2205 08839,2022

5. Cross-attention in coupled unmixing nets for unsupervised hyperspectral super-resolution;yao;Proc Eur Conf Comput Vis (ECCV),2020

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