mlsanityData to 7 Oct 2026

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

arXiv 2102.06531 ↗First posted 9 Feb 2021eess.IV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 9 Feb 2021.

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20 of 46 references.

References · 20

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8 more
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