  Although current deep learning-based methods have gained promising
performance in the blind single image super-resolution (SISR) task, most of
them mainly focus on heuristically constructing diverse network architectures
and put less emphasis on the explicit embedding of the physical generation
mechanism between blur kernels and high-resolution (HR) images. To alleviate
this issue, we propose a model-driven deep neural network, called KXNet, for
blind SISR. Specifically, to solve the classical SISR model, we propose a
simple-yet-effective iterative algorithm. Then by unfolding the involved
iterative steps into the corresponding network module, we naturally construct
the KXNet. The main specificity of the proposed KXNet is that the entire
learning process is fully and explicitly integrated with the inherent physical
mechanism underlying this SISR task. Thus, the learned blur kernel has clear
physical patterns and the mutually iterative process between blur kernel and HR
image can soundly guide the KXNet to be evolved in the right direction.
Extensive experiments on synthetic and real data finely demonstrate the
superior accuracy and generality of our method beyond the current
representative state-of-the-art blind SISR methods. Code is available at:
https://github.com/jiahong-fu/KXNet.
