  Current deep image super-resolution (SR) approaches attempt to restore
high-resolution images from down-sampled images or by assuming degradation from
simple Gaussian kernels and additive noises. However, such simple image
processing techniques represent crude approximations of the real-world
procedure of lowering image resolution. In this paper, we propose a more
realistic process to lower image resolution by introducing a new Kernel
Adversarial Learning Super-resolution (KASR) framework to deal with the
real-world image SR problem. In the proposed framework, degradation kernels and
noises are adaptively modeled rather than explicitly specified. Moreover, we
also propose an iterative supervision process and high-frequency selective
objective to further boost the model SR reconstruction accuracy. Extensive
experiments validate the effectiveness of the proposed framework on real-world
datasets.
