Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods. Hence, the state of the art in image restoration and compression is getting redefined. This special issue covers the state of the art in learned image/video restoration and compression to promote further progress in innovative architectures and training methods for effective and efficient networks for image/video restoration and compression.
@article{arxiv.2102.06531,
title = {Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression},
author = {A. Murat Tekalp and Michele Covell and Radu Timofte and Chao Dong},
journal= {arXiv preprint arXiv:2102.06531},
year = {2021}
}