  Blind image super-resolution (BISR) aims to reconstruct a high-resolution
image from its low-resolution counterpart degraded by unknown blur kernel and
noise. Many deep neural network based methods have been proposed to tackle this
challenging problem without considering the image degradation model. However,
they largely rely on the training sets and often fail to handle images with
unseen blur kernels during inference. Deep unfolding methods have also been
proposed to perform BISR by utilizing the degradation model. Nonetheless, the
existing deep unfolding methods cannot explicitly solve the data term of the
unfolding objective function, limiting their capability in blur kernel
estimation. In this work, we propose a novel unfolded deep kernel estimation
(UDKE) method, which, for the first time to our best knowledge, explicitly
solves the data term with high efficiency. The UDKE based BISR method can
jointly learn image and kernel priors in an end-to-end manner, and it can
effectively exploit the information in both training data and image degradation
model. Experiments on benchmark datasets and real-world data demonstrate that
the proposed UDKE method could well predict complex unseen non-Gaussian blur
kernels in inference, achieving significantly better BISR performance than
state-of-the-art. The source code of UDKE is available at:
https://github.com/natezhenghy/UDKE.
