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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatial information</i> from RGB images to guide the reconstruction process, and ii) the enhancement of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectral consistency</i> 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.