  Blind single image super-resolution (SISR) is a challenging task in image
processing due to the ill-posed nature of the inverse problem. Complex
degradations present in real life images make it difficult to solve this
problem using na\"ive deep learning approaches, where models are often trained
on synthetically generated image pairs. Most of the effort so far has been
focused on solving the inverse problem under some constraints, such as for a
limited space of blur kernels and/or assuming noise-free input images. Yet,
there is a gap in the literature to provide a well-generalized deep
learning-based solution that performs well on images with unknown and highly
complex degradations. In this paper, we propose IKR-Net (Iterative Kernel
Reconstruction Network) for blind SISR. In the proposed approach, kernel and
noise estimation and high-resolution image reconstruction are carried out
iteratively using dedicated deep models. The iterative refinement provides
significant improvement in both the reconstructed image and the estimated blur
kernel even for noisy inputs. IKR-Net provides a generalized solution that can
handle any type of blur and level of noise in the input low-resolution image.
IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy
images with motion blur.
