SST-ReversibleNet: Reversible-prior-based Spectral-Spatial Transformer
  for Efficient Hyperspectral Image Reconstruction
Zeyu Cai Jian Yu Ziyu Zhang Chengqian Jin Feipeng Da
Spectral image reconstruction is an important task in snapshot compressed
imaging. This paper aims to propose a new end-to-end framework with iterative
capabilities similar to a deep unfolding network to improve reconstruction
accuracy, independent of optimization conditions, and to reduce the number of
parameters. A novel framework called the reversible-prior-based method is
proposed. Inspired by the reversibility of the optical path, the
reversible-prior-based framework projects the reconstructions back into the
measurement space, and then the residuals between the projected data and the
real measurements are fed into the network for iteration. The reconstruction
subnet in the network then learns the mapping of the residuals to the true
values to improve reconstruction accuracy. In addition, a novel
spectral-spatial transformer is proposed to account for the global correlation
of spectral data in both spatial and spectral dimensions while balancing
network depth and computational complexity, in response to the shortcomings of
existing transformer-based denoising modules that ignore spatial texture
features or learn local spatial features at the expense of global spatial
features. Extensive experiments show that our SST-ReversibleNet significantly
outperforms state-of-the-art methods on simulated and real HSI datasets, while
requiring lower computational and storage costs.
https://github.com/caizeyu1992/SST