  While researches on model-based blind single image super-resolution (SISR)
have achieved tremendous successes recently, most of them do not consider the
image degradation sufficiently. Firstly, they always assume image noise obeys
an independent and identically distributed (i.i.d.) Gaussian or Laplacian
distribution, which largely underestimates the complexity of real noise.
Secondly, previous commonly-used kernel priors (e.g., normalization, sparsity)
are not effective enough to guarantee a rational kernel solution, and thus
degenerates the performance of subsequent SISR task. To address the above
issues, this paper proposes a model-based blind SISR method under the
probabilistic framework, which elaborately models image degradation from the
perspectives of noise and blur kernel. Specifically, instead of the traditional
i.i.d. noise assumption, a patch-based non-i.i.d. noise model is proposed to
tackle the complicated real noise, expecting to increase the degrees of freedom
of the model for noise representation. As for the blur kernel, we novelly
construct a concise yet effective kernel generator, and plug it into the
proposed blind SISR method as an explicit kernel prior (EKP). To solve the
proposed model, a theoretically grounded Monte Carlo EM algorithm is
specifically designed. Comprehensive experiments demonstrate the superiority of
our method over current state-of-the-arts on synthetic and real datasets. The
source code is available at https://github.com/zsyOAOA/BSRDM.
