  With the development of deep learning technology, multi-spectral image
super-resolution methods based on convolutional neural network have recently
achieved great progress. However, the single hyperspectral image
super-resolution remains a challenging problem due to the high-dimensional and
complex spectral characteristics of hyperspectral data, which make it difficult
to simultaneously capture spatial and spectral information. To deal with this
issue, we propose a novel Feedback Refined Local-Global Network (FRLGN) for the
super-resolution of hyperspectral image. To be specific, we develop a new
Feedback Structure and a Local-Global Spectral Block to alleviate the
difficulty in spatial and spectral feature extraction. The Feedback Structure
can transfer the high-level information to guide the generation process of
low-level feature, which is achieved by a recurrent structure with finite
unfoldings. Furthermore, in order to effectively use the high-level information
passed back, a Local-Global Spectral Block is constructed to handle the
feedback connections. The Local-Global Spectral Block utilizes the feedback
high-level information to correct the low-level feature from local spectral
bands and generates powerful high-level representations among global spectral
bands. By incorporating the Feedback Structure and Local-Global Spectral Block,
the FRLGN can fully exploit spatial-spectral correlations among spectral bands
and gradually reconstruct high-resolution hyperspectral images. The source code
of FRLGN is available at https://github.com/tangzhenjie/FRLGN.
