ReDIP: Rethinking Deep Image Prior for Compressive Spectral Imaging Calibration

Emmanuel Martinez, Jorge Bacca, Tatiana Gelvez-Barrera, Henry Arguello

Abstract: Learning-based reconstruction methods have shown state-of-the-art (SOTA) performance for compressive spectral imaging (CSI). However, the training setup dependence causes slight variations in the scene's statistical distribution or calibration errors in the sensing matrix producing poor testing reconstruction quality. Therefore, we propose a computational calibration methodology to improve any CSI reconstruction deep neural network (DNN) testing quality and robustness. Specifically, similar to transfer learning, we rethink the deep image prior framework to retrain a SOTA DNN for a particular CSI measurement without retraining the DNN from scratch. Experimental results show an average peak signal-to-noise ratio improvement of 5.5dB when slight variations in the CSI measurement and calibration errors in the sensing matrix are considered. Additionally, even when no variations are considered, the proposed calibration methodology improves in up to 2.3dB the reconstruction quality.