Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution
Author:
Affiliation:
1. Electronic Information School, Wuhan University, Wuhan, China
2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Funder
National Natural Science Foundation of China
Key Research and Development Program of Hubei Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computational Mathematics,Computer Science Applications,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6745852/9679468/10011536.pdf?arnumber=10011536
Reference41 articles.
1. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
2. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
3. Spatial-spectral residual network for hyperspectral image super-resolution;wang,2020
4. Dual 1D-2D Spatial-Spectral CNN for Hyperspectral Image Super-Resolution
5. Learning Spectral and Spatial Features Based on Generative Adversarial Network for Hyperspectral Image Super-Resolution
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