Panchromatic‐Guided Self‐Supervised Framework for Dual‐Camera Compressive Spectral Imaging

ABSTRACT Coded aperture snapshot spectral imaging (CASSI) faces the ill‐posed inverse problem, and reconstruction accuracy remains unsatisfactory despite various regularizers. Dual‐camera CASSI (DC‐CASSI) mitigates this problem by capturing panchromatic (PAN) images alongside compressed measurements. Although untrained deep neural networks (DNN) can leverage PAN imagery, they often struggle to preserve spectral integrity and ensure optimization stability. To address these dual issues, this paper proposes a panchromatic‐guided self‐supervised framework (PGSF). Its core is a novel spectral low‐rank guided spatial enhancement module (SLGSEM), which performs dual spatial‐semantic enhancement of network inputs by fusing PAN guidance with a learned low‐rank spectral prior. This module seamlessly integrates with various untrained DNN (joint network). Furthermore, the joint network is embedded as a regularizer within an ADMM‐based optimization algorithm, enabling self‐supervised parameter learning and progressive input refinement throughout iterations. Extensive experiments demonstrate that PGSF significantly enhances untrained DNN performance, achieving state‐of‐the‐art reconstruction in both quantitative metrics and visual quality without requiring any training data.