Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means.
We argue that in doing so, the challenge ranks the proposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to the high-resolution target.
We propose a self-supervised approach for training multi-frame video denoising networks.
Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images.
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
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