Deep plug-and-play self-supervised neural networks for spectral snapshot compressive imaging

The encoding aperture snapshot spectral imaging system, based on the compressive sensing theory, can be regarded as an encoder, which can efficiently obtain compressed two-dimensional spectral data and then decode it into three-dimensional spectral data through deep neural networks. However, training the deep neural networks requires a large amount of clean data that is difficult to obtain. To address the problem of insufficient training data for deep neural networks, a self-supervised hyperspectral denoising neural network based on neighborhood sampling is proposed. This network is integrated into a deep plug-and-play framework to achieve self-supervised spectral reconstruction. The study also examines the impact of different noise degradation models on the final reconstruction quality. Experimental results demonstrate that the self-supervised learning method enhances the average peak signal-to-noise ratio by 1. 18 dB and improves the structural similarity by 0. 009 compared with the supervised learning method. Additionally, it achieves better visual reconstruction results.