论文标题
Cisrnet：压缩图像超分辨率网络
CISRNet: Compressed Image Super-Resolution Network
论文作者
论文摘要
近年来，已经对单图超分辨率（SISR）进行了大量研究。但是，据我们所知，这些研究中很少有人主要集中在压缩图像上。尽管具有高实用值，但复杂的压缩伪像等问题仍阻碍了这项研究的进步。为了解决这个问题，我们提出了Cisrnet；采用两阶段的粗到十五学习框架的网络，该框架主要针对压缩图像超分辨率问题进行了优化。具体而言，Cisrnet由两个主要子网组成。粗糙和精炼网络，分别在这两个网络中使用了递归和残差学习。广泛的实验表明，通过仔细的设计选择，Cisrnet与压缩图像超分辨率任务中竞争的单像超分辨率方法相比表现出色。
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.
