A Range-Null Space Decomposition Approach for Fast and Flexible Spectral
  Compressive Imaging
Junyu Wang Shijie Wang Ruijie Zhang Zengqiang Zheng Wenyu Liu Xinggang Wang
We present RND-SCI, a novel framework for compressive hyperspectral image
(HSI) reconstruction. Our framework decomposes the reconstructed object into
range-space and null-space components, where the range-space part ensures the
solution conforms to the compression process, and the null-space term
introduces a deep HSI prior to constraining the output to have satisfactory
properties. RND-SCI is not only simple in design with strong interpretability
but also can be easily adapted to various HSI reconstruction networks,
improving the quality of HSIs with minimal computational overhead. RND-SCI
significantly boosts the performance of HSI reconstruction networks in
retraining, fine-tuning or plugging into a pre-trained off-the-shelf model.
Based on the framework and SAUNet, we design an extremely fast HSI
reconstruction network, RND-SAUNet, which achieves an astounding 91 frames per
second while maintaining superior reconstruction accuracy compared to other
less time-consuming methods. Code and models are available at
https://github.com/hustvl/RND-SCI.