  Real-world image super-resolution (RISR) has received increased focus for
improving the quality of SR images under unknown complex degradation. Existing
methods rely on the heavy SR models to enhance low-resolution (LR) images of
different degradation levels, which significantly restricts their practical
deployments on resource-limited devices. In this paper, we propose a novel
Dynamic Channel Splitting scheme for efficient Real-world Image
Super-Resolution, termed DCS-RISR. Specifically, we first introduce the light
degradation prediction network to regress the degradation vector to simulate
the real-world degradations, upon which the channel splitting vector is
generated as the input for an efficient SR model. Then, a learnable octave
convolution block is proposed to adaptively decide the channel splitting scale
for low- and high-frequency features at each block, reducing computation
overhead and memory cost by offering the large scale to low-frequency features
and the small scale to the high ones. To further improve the RISR performance,
Non-local regularization is employed to supplement the knowledge of patches
from LR and HR subspace with free-computation inference. Extensive experiments
demonstrate the effectiveness of DCS-RISR on different benchmark datasets. Our
DCS-RISR not only achieves the best trade-off between computation/parameter and
PSNR/SSIM metric, and also effectively handles real-world images with different
degradation levels.
