  Traditional single image super-resolution (SISR) methods that focus on
solving single and uniform degradation (i.e., bicubic down-sampling), typically
suffer from poor performance when applied into real-world low-resolution (LR)
images due to the complicated realistic degradations. The key to solving this
more challenging real image super-resolution (RealSR) problem lies in learning
feature representations that are both informative and content-aware. In this
paper, we propose an Omni-frequency Region-adaptive Network (ORNet) to address
both challenges, here we call features of all low, middle and high frequencies
omni-frequency features. Specifically, we start from the frequency perspective
and design a Frequency Decomposition (FD) module to separate different
frequency components to comprehensively compensate the information lost for
real LR image. Then, considering the different regions of real LR image have
different frequency information lost, we further design a Region-adaptive
Frequency Aggregation (RFA) module by leveraging dynamic convolution and
spatial attention to adaptively restore frequency components for different
regions. The extensive experiments endorse the effective, and scenario-agnostic
nature of our OR-Net for RealSR.
