Dual-camera compressive hyperspectral imaging (DC-CHI) offers the capability to reconstruct 3D hyperspectral image (HSI) by fusing compressive and panchromatic (PAN) image, which has shown great potential for snapshot hyperspectral imaging in practice. In this paper, we introduce a novel DCCHI reconstruction network, intra-inter similarity exploiting Transformer (In2SET). Our key insight is to make full use of the PAN image to assist the reconstruction. To this end, we propose to use the intra-similarity within the PAN image as a proxy for approximating the intra-similarity in the original HSI, thereby offering an enhanced content prior for more accurate HSI reconstruction. Furthermore, we propose to use the inter-similarity to align the features between HSI and PAN images, thereby maintaining semantic consistency between the two modalities during the reconstruction process. By integrating In2SET into a PAN-guided deep unrolling (PGDU)framework, our method substantially enhances the spatial-spectral fidelity and detail of the reconstructed images, providing a more comprehensive and accurate depiction of the scene. Experiments conducted on both real and simulated datasets demonstrate that our approach consistently outperforms existing state-of-the-art methods in terms of reconstruction quality and computational complexity. The code is available at https://github.com/2JONAS/In2SET.

Computational spectral imaging has been striving to capture the spectral information of the dynamic world in the last few decades. In this paper, we propose an interpretable neural network for computational spectral imaging. First, we introduce a novel data-driven prior that can adaptively exploit both the local and non-local correlations among the spectral image. Our data-driven prior is integrated as a regularizer into the reconstruction problem. Then, we propose to unroll the reconstruction problem into an optimization-inspired deep neural network. The architecture of the network has high interpretability by explicitly characterizing the image correlation and the system imaging model. Finally, we learn the complete parameters in the network through end-to-end training, enabling robust performance with high spatial-spectral fidelity. Extensive simulation and hardware experiments validate the superior performance of our method over state-of-the-art methods.