CISRNet: Compressed Image Super-Resolution Network
This addresses a practical problem for image processing applications where compressed images need enhancement, but it is incremental as it adapts existing super-resolution techniques to a specific domain.
The paper tackles the problem of super-resolution for compressed images, which is hindered by compression artifacts, and proposes CISRNet, a two-stage network that performs favorably against competing methods in compressed image super-resolution tasks.
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.