Real-World Image Super-Resolution Via Spatio-Temporal Correlation Network
Hongyang Zhou, Xiaobin Zhu, Han Zheng, Xu-Cheng Yin · 2021
Super-resolving real-world image is very challenging due to the degradations in real-world low-resolution images are highly complicated. In this paper, we propose a novel Spatio-temporal Correlation Network (STCN) for real-world single image super-resolution. Specifically, we adopt a very deep network which consists of several attention groups. Each attention group (AG) contains a series of residual channel attention blocks (RCABs) and one spatio-temporal correlation block (STCB). Notably, STCB mainly consists of a residual 3D convolution, and aims to fully explore the local spatial and temporal correlations between channels of feature maps generated by RCABs for selectively capturing more informative features. In addition, we propose an innovative dual restriction (DR) through a simple degradation model to reduce the possible space of mapping functions in super-resolution. Experiments conducted on two public available real-world datasets demonstrate the superior performance of our method.