  Most current deep learning based single image super-resolution (SISR) methods
focus on designing deeper / wider models to learn the non-linear mapping
between low-resolution (LR) inputs and the high-resolution (HR) outputs from a
large number of paired (LR/HR) training data. They usually take as assumption
that the LR image is a bicubic down-sampled version of the HR image. However,
such degradation process is not available in real-world settings i.e. inherent
sensor noise, stochastic noise, compression artifacts, possible mismatch
between image degradation process and camera device. It reduces significantly
the performance of current SISR methods due to real-world image corruptions. To
address these problems, we propose a deep Super-Resolution Residual
Convolutional Generative Adversarial Network (SRResCGAN) to follow the
real-world degradation settings by adversarial training the model with
pixel-wise supervision in the HR domain from its generated LR counterpart. The
proposed network exploits the residual learning by minimizing the energy-based
objective function with powerful image regularization and convex optimization
techniques. We demonstrate our proposed approach in quantitative and
qualitative experiments that generalize robustly to real input and it is easy
to deploy for other down-scaling operators and mobile/embedded devices.
