Prior Images Guided Generative Autoencoder Model for Dual-Camera Compressive Spectral Imaging

Yurong Chen, Yaonan Wang, Hui Zhang

Abstract: Compressive Spectral Imaging (CSI) techniques have attracted considerable attention among researchers for their ability to simultaneously capture spatial and spectral information using low-cost, compact optical components. A prominent example of CSI techniques is the Dual-Camera Coded Aperture Snapshot Spectral Imaging (DC-CASSI), which involves reconstructing hyperspectral images from CASSI measurements and uncoded panchromatic or RGB images. Despite its significance, the reconstruction process in DC-CASSI is challenging. Conventional DC-CASSI techniques rely on different models to explore the similarity between uncoded images and hyperspectral images. Nevertheless, two main issues persist: i) the effective utilization of spatial information from RGB images to guide the reconstruction process, and ii) the enhancement of spectral consistency of recovered images when using panchromatic/RGB images, which inherently lack precise spectral information. To address these challenges, we propose a novel Prior images guided generative autoEncoder (PiE) model. The PiE model leverages RGB images as prior information to enhance spatial details and designs a generative model to improve spectral quality. Notably, the generative model is optimized in a self-supervised manner. Comprehensive experimental results demonstrate that the proposed PiE method outperforms existing techniques, achieving state-of-the-art performance.