  Recently, deep neural network models have achieved impressive results in
various research fields. Come with it, an increasing number of attentions have
been attracted by deep super-resolution (SR) approaches. Many existing methods
attempt to restore high-resolution images from directly down-sampled
low-resolution images or with the assumption of Gaussian degradation kernels
with additive noises for their simplicities. However, in real-world scenarios,
highly complex kernels and non-additive noises may be involved, even though the
distorted images are visually similar to the clear ones. Existing SR models are
facing difficulties to deal with real-world images under such circumstances. In
this paper, we introduce a new kernel agnostic SR framework to deal with
real-world image SR problem. The framework can be hanged seamlessly to multiple
mainstream models. In the proposed framework, the degradation kernels and
noises are adaptively modeled rather than explicitly specified. Moreover, we
also propose an iterative supervision process and frequency-attended objective
from orthogonal perspectives to further boost the performance. The experiments
validate the effectiveness of the proposed framework on multiple real-world
datasets.
