  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.
