Side information-guided deep unfolding network based on self-supervised learning for dual-camera compressive hyperspectral imaging

Abstract

Dual-Camera Compressive Hyperspectral Imaging (DCCHI) extends the CASSI system by adding a complementary panchromatic (PAN) camera to enhance hyperspectral image (HSI) reconstruction. Its potential broad applications make it attract great attentions. However, current DCCHI reconstruction methods face several challenges: 1) Existing regularization methods, based on hand-crafted priors, struggle to capture the complex intrinsic structure of HSI. 2) Deep learning-based approaches require large amounts of ground truth data for training. 3) The rich structural information in PAN images is underutilized. To address these issues, in this work, a novel side information-guided deep unfolding self-supervised network model is proposed to reconstruct HSI solely from compressed snapshot measurements and PAN images. Firstly, a Guided Deep Spatial-Spectral Attention Network (GDSSAN) is designed as a regularizer for the DCCHI reconstruction problem. This network effectively captures deep spatial-spectral features of HSIs, leveraging the non-linear capabilities of neural networks. Secondly, a Guided Feature Extraction Pyramid (GFEP) Block is constructed to extract multi-scale features from the PAN image, guiding both the encoding and decoding processes at different scales to enhance the robustness and fidelity of HSI reconstruction. Thirdly, a dual-domain hybrid loss function is introduced, which integrates the physical imaging mechanism of DCCHI with the spatial-spectral fidelity of HSIs, further enhancing the model's reconstruction performance. Extensive simulations and real-world experiments demonstrate that the proposed method adapts to various experimental scenes without training requirement, showcasing its strong generalization ability. Moreover, it significantly outperforms existing state-of-the-art (SOTA) methods in both quantitative metrics and visual comparisons.