  Deep learning based methods have dominated super-resolution (SR) field due to
their remarkable performance in terms of effectiveness and efficiency. Most of
these methods assume that the blur kernel during downsampling is
predefined/known (e.g., bicubic). However, the blur kernels involved in real
applications are complicated and unknown, resulting in severe performance drop
for the advanced SR methods. In this paper, we propose an Iterative Kernel
Correction (IKC) method for blur kernel estimation in blind SR problem, where
the blur kernels are unknown. We draw the observation that kernel mismatch
could bring regular artifacts (either over-sharpening or over-smoothing), which
can be applied to correct inaccurate blur kernels. Thus we introduce an
iterative correction scheme -- IKC that achieves better results than direct
kernel estimation. We further propose an effective SR network architecture
using spatial feature transform (SFT) layers to handle multiple blur kernels,
named SFTMD. Extensive experiments on synthetic and real-world images show that
the proposed IKC method with SFTMD can provide visually favorable SR results
and the state-of-the-art performance in blind SR problem.
