SAKE: Unsupervised HSI Super-Resolution via Adaptive Kernel Estimation and Reconstruction

Methods for hyperspectral image (HSI) super-resolution employing deep learning have been pivotal in a range of fields. Despite this, many current approaches face challenges like the scarcity of paired datasets, simplified degradation models, and a lack of image prior information, leading to weak generalization over different datasets and degradation conditions. To overcome these challenges, this paper introduces a single hyperspectral image blind super-resolution algorithm supplemented by an unsupervised blur kernel estimation module. Random kernels from Gaussian distributions form pseudo-label kernels to handle arbitrary degradation kernels. These are processed by a DIP-based network, channel-by-channel, using a batch gradient acceleration algorithm for network parameter updates. This unsupervised, pre-training-free method achieves single HSI super-resolution through alternating iterative optimization. This method necessitates solely the input of the degraded image and requires no extra data, demonstrating a minimal reliance on data. Simulated experiments across datasets and scenarios demonstrate the proposed method’s superior ability to estimate degradation blur kernels, outperforming existing state-of-the-art methods.