  Learning-based single image super-resolution (SISR) methods are continuously
showing superior effectiveness and efficiency over traditional model-based
methods, largely due to the end-to-end training. However, different from
model-based methods that can handle the SISR problem with different scale
factors, blur kernels and noise levels under a unified MAP (maximum a
posteriori) framework, learning-based methods generally lack such flexibility.
To address this issue, this paper proposes an end-to-end trainable unfolding
network which leverages both learning-based methods and model-based methods.
Specifically, by unfolding the MAP inference via a half-quadratic splitting
algorithm, a fixed number of iterations consisting of alternately solving a
data subproblem and a prior subproblem can be obtained. The two subproblems
then can be solved with neural modules, resulting in an end-to-end trainable,
iterative network. As a result, the proposed network inherits the flexibility
of model-based methods to super-resolve blurry, noisy images for different
scale factors via a single model, while maintaining the advantages of
learning-based methods. Extensive experiments demonstrate the superiority of
the proposed deep unfolding network in terms of flexibility, effectiveness and
also generalizability.
