  Image super-resolution (SR) is a technique to recover lost high-frequency
information in low-resolution (LR) images. Spatial-domain information has been
widely exploited to implement image SR, so a new trend is to involve
frequency-domain information in SR tasks. Besides, image SR is typically
application-oriented and various computer vision tasks call for image arbitrary
magnification. Therefore, in this paper, we study image features in the
frequency domain to design a novel scale-arbitrary image SR network. First, we
statistically analyze LR-HR image pairs of several datasets under different
scale factors and find that the high-frequency spectra of different images
under different scale factors suffer from different degrees of degradation, but
the valid low-frequency spectra tend to be retained within a certain
distribution range. Then, based on this finding, we devise an adaptive
scale-aware feature division mechanism using deep reinforcement learning, which
can accurately and adaptively divide the frequency spectrum into the
low-frequency part to be retained and the high-frequency one to be recovered.
Finally, we design a scale-aware feature recovery module to capture and fuse
multi-level features for reconstructing the high-frequency spectrum at
arbitrary scale factors. Extensive experiments on public datasets show the
superiority of our method compared with state-of-the-art methods.
