Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
This special issue addresses the problem of advancing image/video restoration and compression for researchers and practitioners by showcasing state-of-the-art learned models.
This editorial introduces a special issue on deep learning for image/video restoration and compression, highlighting that learned models have achieved significant performance gains, particularly in perceptual quality, over traditional methods. The issue aims to promote further progress in innovative architectures and training methods for effective and efficient networks in these domains.
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.