DDIP: Mutual-Regularized Dual Deep Image Prior for Self-Supervised Compressive Spectral Imaging

In recent years, numerous hyperspectral image (HSI) reconstruction methods have been proposed to enhance the imaging quality of coded aperture snapshot spectral compressive imaging (CASSI) systems. Among these methods, self-supervised Deep Image Prior (DIP)-based approaches have gained attention for their ability to reconstruct three-dimensional (3D) HSIs without the need for external training data. However, DIP methods often suffer from overfitting to high-frequency noise during the optimization process, leading to artifacts and loss of fine details. To address these challenges, we propose a Mutual-Regularized Dual Deep Image Prior (DDIP) framework that employs implicit mutual regularization between two DIP networks. By encouraging mutual constraints, DDIP effectively mitigates high-frequency learning bias and suppresses noise amplification. Additionally, we employ a Half Quadratic Splitting (HQS) optimization strategy to ensure stable and efficient convergence, progressively integrating complementary information from the dual networks. We provide a comprehensive convergence analysis of the DDIP framework and establish theoretical conditions to guide the progressive fusion of the dual networks, ensuring robust and reliable reconstruction. Based on the insights from the convergence analysis, we introduce an Adaptive Deep Image Prior inner-loop strategy that dynamically adjusts the inner-loop updates, ensuring balanced learning of low- and high-frequency components. Moreover, a Residual Spectral-Spatial Feature Attention Network (SSFAN) is designed to enhance spectral-spatial feature extraction, further improving reconstruction accuracy. Extensive experiments on benchmark datasets demonstrate that DDIP achieves competitive HSI reconstruction quality compared to state-of-the-art unsupervised and self-supervised methods.