Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
We propose a self-supervised approach for training multi-frame video denoising networks.
VBM3D is an extension to video of the well known image denoising algorithm BM3D, which takes advantage of the sparse representation of stacks of similar patches in a transform domain.
Detecting reliably copy-move forgeries is difficult because images do contain similar objects.
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.