A Range-Null Space Decomposition Approach for Fast and Flexible Spectral Compressive Imaging
This work addresses faster and more accurate hyperspectral imaging for applications like remote sensing or medical diagnostics, representing an incremental improvement through a novel decomposition approach.
The paper tackles hyperspectral image reconstruction by proposing RND-SCI, a framework that decomposes the reconstruction into range-space and null-space components, resulting in improved accuracy with minimal computational overhead, achieving 91 frames per second while maintaining superior reconstruction quality.
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.