  Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution
(LR) images degraded by arbitrary degradations, SR with the degradation model
is required. However, this paper reveals that non-blind SR that is trained
simply with various blur kernels exhibits comparable performance as those with
the degradation model for blind SR. This result motivates us to revisit
high-performance non-blind SR and extend it to blind SR with blur kernels. This
paper proposes two SR networks by integrating kernel estimation and SR branches
in an iterative end-to-end manner. In the first model, which is called the
Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel
representations are estimated for conditioning the SR branch. In our second
model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated
and directly employed for modeling the image degradation. The estimated kernel
is employed not only for back-propagating its residual but also for
forward-propagating the residual to iterative stages. This forward-propagation
encourages these stages to learn a variety of different features in different
stages by focusing on pixels with large residuals in each stage. Experimental
results validate the effectiveness of our proposed networks for kernel
estimation and SR. We will release the code for this work.
