mlsanityData to 7 Oct 2026

CISRNet: Compressed Image Super-Resolution Network

arXiv 2201.06045 ↗First posted 16 Jan 2022eess.IV cs.CVcited by 1

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Abstract (arXiv)

In recent years, tons of research has been conducted on Single Image Super-Resolution (SISR). However, to the best of our knowledge, few of these studies are mainly focused on compressed images. A problem such as complicated compression artifacts hinders the advance of this study in spite of its high practical values. To tackle this problem, we proposed CISRNet; a network that employs a two-stage coarse-to-fine learning framework that is mainly optimized for Compressed Image Super-Resolution Problem. Specifically, CISRNet consists of two main subnetworks; the coarse and refinement network, where recursive and residual learning is employed within these two networks respectively. Extensive experiments show that with a careful design choice, CISRNet performs favorably against competing Single-Image Super-Resolution methods in the Compressed Image Super-Resolution tasks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 16 Jan 2022.

Builds on

26 of 45 references.

References · 26

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14 more
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Citing papers per year: 2023: 1.

1 paper.

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