HYPERSPECTRAL IMAGE SUPER-RESOLUTION VIA MULTI-DOMAIN FEATURE LEARNING

Hyperspectral image super-resolution (SR) methods are continually being refreshed due to deep neural networks. Despite this, the existing works barely explore more spatial information using mixed 2D/3D convolution. Moreover, they do not make full use of multi-domain features to realize information complementation. To tackle these challenges, we propose a hyperspectral image SR approach via multi-domain feature learning. To be specific, a multi-domain feature learning strategy using 2D/3D unit is presented to explore spatial and spectral information by alternate manner. To recover the more details, the edge body generation mechanism (EBGM) is introduced to learn the high frequency information, which generates the edge prior. Besides, the multi-domain feature fusion (MDFF) is designed to fully integrated hierarchical knowledge from different 2D/3D units, leading to further achieve information complementation. Experiments demonstrate that our approach attains the better performance over the state-of-the-art methods.