  By developing sophisticated image priors or designing deep(er) architectures,
a variety of image Super-Resolution (SR) approaches have been proposed recently
and achieved very promising performance. A natural question that arises is
whether these methods can be reformulated into a unifying framework and whether
this framework assists in SR reconstruction? In this paper, we present a simple
but effective single image SR method based on ensemble learning, which can
produce a better performance than that could be obtained from any of SR methods
to be ensembled (or called component super-resolvers). Based on the assumption
that better component super-resolver should have larger ensemble weight when
performing SR reconstruction, we present a Maximum A Posteriori (MAP)
estimation framework for the inference of optimal ensemble weights. Specially,
we introduce a reference dataset, which is composed of High-Resolution (HR) and
Low-Resolution (LR) image pairs, to measure the super-resolution abilities
(prior knowledge) of different component super-resolvers. To obtain the optimal
ensemble weights, we propose to incorporate the reconstruction constraint,
which states that the degenerated HR image should be equal to the LR
observation one, as well as the prior knowledge of ensemble weights into the
MAP estimation framework. Moreover, the proposed optimization problem can be
solved by an analytical solution. We study the performance of the proposed
method by comparing with different competitive approaches, including four
state-of-the-art non-deep learning based methods, four latest deep learning
based methods and one ensemble learning based method, and prove its
effectiveness and superiority on three public datasets.
