Region-Based Spectral-Spatial Mutual Induction Network for Hyperspectral Image Reconstruction

Abstract

In hyperspectral compression imaging, the choice of a reconstruction algorithm is critical for achieving high-quality results. Hyperspectral Images (HSI) have strong spectral-spatial correlations within local regions, valuable for reconstruction. However, existing learning-based methods often overlook regional variations by treating the entire image as a whole. To address this, we propose a novel region-based iterative approach for HSI reconstruction. We introduce a deep unfolding method augmented with a Region-based Spectral-Spatial Mutual Induction (RSSMI) network to model regional priors. Our approach involves partitioning the image into regions during each unfolding phase. Within each region, we employ a spatial-guided spectral attention module for holistic spectral relationships and a spectral-guided spatial attention module for spatial details. By leveraging mutual induction, our method simultaneously recovers spectral and spatial information. Furthermore, we address the issue of favoring easy-to-learn regions by introducing Focal Region Loss that dynamically adjusts loss weights for regions, emphasizing those that are harder to reconstruct. Experimental results demonstrate that our method achieves competitive performance and excels in spectrum and texture reconstruction on both simulated and real HSI datasets.