  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 degradation
parameters of the input image, while several convolutional experts with the
same topology are jointly optimized to specify the network parameters via a
non-linear mixture of experts. The joint optimization of multiple experts and
the degradation-adaptive pipeline significantly extend the model capacity to
handle degradations of various levels, while the inference remains efficient
since only one adaptively specified network is used for super-resolving the
input image. Our extensive experiments demonstrate that the proposed DASR is
not only much more effective than existing methods on handling real-world
images with different degradation levels but also efficient for easy
deployment. Codes, models and datasets are available at
https://github.com/csjliang/DASR.
