Deep Error-aware Iterative Optimization Network for Broadband Mosaiced Hyperspectral Imaging

Snapshot hyperspectral imaging based on narrowband mosaic array encoding suffers from limitations such as low signal-to-noise ratio, limited spectral range, and low spatial resolution. To address these challenges, we propose a novel hyperspectral imaging system that integrates broadband mosaic image with high-resolution (HR) panchromatic (PAN) image of the same scene, establishing a new paradigm for HR hyperspectral image (HSI) acquisition. To fully leverage the complementary information from multi-source images, we introduce a Deep Error-aware Iterative Optimization Network (EIONet), which iteratively reduces reconstruction errors to successfully reconstruct images with both high spatial resolution and high spectral quality. Specifically, we design a Hierarchical Error-aware Cube Updating Mechanism (HECUM) that dynamically partitions image regions based on their reconstruction difficulty during iterations. By prioritizing the enhancement of feature representation in high-difficulty areas, it effectively suppresses the accumulation and propagation of errors. Meanwhile, we employ a Physics-Based Spectral Degradation Modeling approach, constructing a spectral response function with well-defined physical meaning to accurately model the degradation process from the target domain to the observation domain. Experimental results on two public datasets demonstrate that EIONet achieves state-of-the-art performance across multiple evaluation metrics. The related code is available at: https://github.com/Xiexieiii/EIONet.