HIGH-QUALITY SELF-SUPERVISED SNAPSHOT HYPERSPECTRAL IMAGING

Hyperspectral image (HSI) reconstruction is about recovering a 3D HSI from its 2D snapshot measurements, to which deep models have become a promising approach. However, most existing studies train deep models on large amounts of organized data, the collection of which can be difficult in many applications. This paper leverages the image priors encoded in untrained neural networks (NNs) to have a self-supervised learning method which is free from training datasets while adaptive to the statistics of a test sample. To induce better image priors and prevent the NN overfitting undesired solutions, we construct an unrolling-based NN equipped with fractional max pooling (FMP). Furthermore, the FMP is used with randomness to enable self-ensemble for reconstruction accuracy improvement. In the experiments, our self-supervised learning approach enjoys high-quality reconstruction and outperforms recent methods including the supervised ones.