  Deep convolutional neural networks (CNNs) have recently achieved great
success for single image super-resolution (SISR) task due to their powerful
feature representation capabilities. The most recent deep learning based SISR
methods focus on designing deeper / wider models to learn the non-linear
mapping between low-resolution (LR) inputs and high-resolution (HR) outputs.
These existing SR methods do not take into account the image observation
(physical) model and thus require a large number of network's trainable
parameters with a great volume of training data. To address these issues, we
propose a deep Iterative Super-Resolution Residual Convolutional Network
(ISRResCNet) that exploits the powerful image regularization and large-scale
optimization techniques by training the deep network in an iterative manner
with a residual learning approach. Extensive experimental results on various
super-resolution benchmarks demonstrate that our method with a few trainable
parameters improves the results for different scaling factors in comparison
with the state-of-art methods.
