Degradation-Aware Self-Supervised Multi-Temporal Super-Resolution

Learning deep super-resolution models without the need for ground truth data at a higher resolution is critical for satellite imaging applications. This is either due to the lack of existing images at better resolution for certain target wavelengths or the existence of significant domain gaps between the images of different satellites. In this paper, we propose a method and neural network architecture for a multi-image super-resolution problem, where each image in the input stack might be affected by a different degradation process. A test-time finetuning procedure allows to dynamically account for the degradations observed for a specific set of LR inputs, improving over baseline results.