Self-Supervised Learning for Image Super-Resolution and Deblurring

arXiv:2312.11232v2 Announce Type: replace-cross
Abstract: Self-supervised methods have recently proved to be nearly as effective as supervised methods in various imaging inverse problems, paving the way for learning-based methods in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. This is the case in magnetic resonance imaging and computed tomography. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone. However, existing approaches …

abstract applications arxiv case cs.cv data eess.iv image imaging medical medical imaging scientific self-supervised learning supervised learning the way truth type