  Hyperspectral images are crucial for many research works. Spectral
super-resolution (SSR) is a method used to obtain high spatial resolution (HR)
hyperspectral images from HR multispectral images. Traditional SSR methods
include model-driven algorithms and deep learning. By unfolding a variational
method, this paper proposes an optimization-driven convolutional neural network
(CNN) with a deep spatial-spectral prior, resulting in physically interpretable
networks. Unlike the fully data-driven CNN, auxiliary spectral response
function (SRF) is utilized to guide CNNs to group the bands with spectral
relevance. In addition, the channel attention module (CAM) and reformulated
spectral angle mapper loss function are applied to achieve an effective
reconstruction model. Finally, experiments on two types of datasets, including
natural and remote sensing images, demonstrate the spectral enhancement effect
of the proposed method. And the classification results on the remote sensing
dataset also verified the validity of the information enhanced by the proposed
method.
