HEDU-Net: Improving Spectral Snapshot Compressive Imaging With Explicable Degradation Compensation

With the widespread adoption of utilizing coded aperture snapshot spectral imaging (CASSI) to acquire hyperspectral images (HSIs), reconstruction algorithms become increasingly important as one of the most critical parts of CASSI. By formulating the iterative optimization problem as a multistage end-to-end neural network, the deep unfolding framework has made gratifying progress in improving the reconstruction reliability and fidelity. Nevertheless, there still exist three knotty issues limiting the performance of current reconstruction algorithms: 1) predetermined sensing matrix ignoring the gap between real and ideal degradation procedure. 2) ill-structured feature extraction and fusion network making it difficult to effectively recover the spatial texture and ensure spectral consistency. 3) inadequate stage interaction module, which hinders the transmission of useful information. To address these issues, in this paper, we propose a hyperspectral explicable degradation-compensatory unfolding neural network (HEDUNet), which attempts to dynamically compensate for the differences between ideal and real degradation procedures using a residual network under the deep unfolding strategy. Moreover, a spatial-spectral interaction transformer is designed via plugging the Swin transformer under the framework of the MixS2 transformer to independently excavate spatial or spectral features and fuse them thoroughly. Finally, to mitigate the information loss between adjacent stages, stage interaction is also enhanced by proposing a specific feature aggregation module based on the multi-scale attention mechanism. Experiments on both simulation and practical datasets show the state-of-the-art (SOTA) performance and wide application prospects of our method.