Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression

TL;DR

This special issue reviews recent advances in deep learning techniques for image and video restoration and compression, highlighting improved perceptual quality and innovative architectures that redefine the state of the art.

Contribution

It compiles and discusses the latest developments in learned models for image/video restoration and compression, emphasizing new architectures and training methods.

Findings

Learned models outperform traditional methods in perceptual quality.

Innovative architectures enable more effective and efficient restoration and compression.

The issue promotes further research in deep learning for image/video processing.

Abstract

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.