Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
Paper
Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
Jie Liang*, Hui Zeng*, and Lei Zhang.
In arxiv preprint.
Abstract
Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task due to
the unknown complex degradation of real-world images and the limited computation resources in practical applications.
Recent research on Real-ISR has achieved significant progress by modeling the image degradation space; however,
these methods largely rely on heavy backbone networks and they are inflexible to handle images of different degradation levels.
In this paper, we propose an efficient and effective degradation-adaptive super-resolution (DASR) network,
whose parameters are adaptively specified by estimating the degradation of each input image.
Specifically, a tiny regression network is employed to predict the