  Recently, single gray/RGB image super-resolution (SR) methods based on deep
learning have achieved great success. However, there are two obstacles to limit
technical development in the single hyperspectral image super-resolution. One
is the high-dimensional and complex spectral patterns in hyperspectral image,
which make it difficult to explore spatial information and spectral information
among bands simultaneously. The other is that the number of available
hyperspectral training samples is extremely small, which can easily lead to
overfitting when training a deep neural network. To address these issues, in
this paper, we propose a novel Spatial-Spectral Feedback Network (SSFN) to
refine low-level representations among local spectral bands with high-level
information from global spectral bands. It will not only alleviate the
difficulty in feature extraction due to high dimensional of hyperspectral data,
but also make the training process more stable. Specifically, we use hidden
states in an RNN with finite unfoldings to achieve such feedback manner. To
exploit the spatial and spectral prior, a Spatial-Spectral Feedback Block
(SSFB) is designed to handle the feedback connections and generate powerful
high-level representations. The proposed SSFN comes with a early predictions
and can reconstruct the final high-resolution hyperspectral image step by step.
Extensive experimental results on three benchmark datasets demonstrate that the
proposed SSFN achieves superior performance in comparison with the
state-of-the-art methods. The source code is available at
https://github.com/tangzhenjie/SSFN.
