  Single Image Super-Resolution (SISR) is one of the low-level computer vision
problems that has received increased attention in the last few years. Current
approaches are primarily based on harnessing the power of deep learning models
and optimization techniques to reverse the degradation model. Owing to its
hardness, isotropic blurring or Gaussians with small anisotropic deformations
have been mainly considered. Here, we widen this scenario by including large
non-Gaussian blurs that arise in real camera movements. Our approach leverages
the degradation model and proposes a new formulation of the Convolutional
Neural Network (CNN) cascade model, where each network sub-module is
constrained to solve a specific degradation: deblurring or upsampling. A new
densely connected CNN-architecture is proposed where the output of each
sub-module is restricted using some external knowledge to focus it on its
specific task. As far we know this use of domain-knowledge to module-level is a
novelty in SISR. To fit the finest model, a final sub-module takes care of the
residual errors propagated by the previous sub-modules. We check our model with
three state of the art (SOTA) datasets in SISR and compare the results with the
SOTA models. The results show that our model is the only one able to manage our
wider set of deformations. Furthermore, our model overcomes all current SOTA
methods for a standard set of deformations. In terms of computational load, our
model also improves on the two closest competitors in terms of efficiency.
Although the approach is non-blind and requires an estimation of the blur
kernel, it shows robustness to blur kernel estimation errors, making it a good
alternative to blind models.
