SGDE: Self-supervised Geometry Degradation Estimation Framework for Coded Aperture Compressive Spectral Imaging

Coded Aperture Snapshot Spectral Imaging (CASSI) has emerged as a prominent technique for efficient hyperspectral imaging. However, the strong coupling between physical encoding and computational decoding makes CASSI highly sensitive to minor hardware misalignments, which can significantly degrade reconstruction quality. Existing methods either assume ideal imaging conditions, or rely on offline calibration, making them vulnerable to dynamic perturbations, such as thermal expansion and mechanical vibration that cause mask shifts. To address these limitations, we propose a Self-Supervised Geometry Degradation Estimation (SGDE) framework that explicitly models mask misalignments as an affine transformation and embeds it into the imaging model. SGDE jointly estimates affine parameters and reconstructs the hyperspectral image in a self-supervised manner, eliminating the need for calibration targets or device-specific training data. Furthermore, we introduce a multi-kernel estimation strategy to enhance calibration robustness under large perturbations. Extensive experiments on both simulated and real-world datasets demonstrate that SGDE achieves superior robustness against geometric degradations. Moreover, the estimated affine parameters can be directly integrated into existing reconstruction algorithms, enabling plug-and-play calibration for practical CASSI systems.