Fortifying Fully Convolutional Generative Adversarial Networks for Image
  Super-Resolution Using Divergence Measures
Arkaprabha Basu Kushal Bose Sankha Subhra Mullick Anish Chakrabarty Swagatam Das
Super-Resolution (SR) is a time-hallowed image processing problem that aims
to improve the quality of a Low-Resolution (LR) sample up to the standard of
its High-Resolution (HR) counterpart. We aim to address this by introducing
Super-Resolution Generator (SuRGe), a fully-convolutional Generative
Adversarial Network (GAN)-based architecture for SR. We show that distinct
convolutional features obtained at increasing depths of a GAN generator can be
optimally combined by a set of learnable convex weights to improve the quality
of generated SR samples. In the process, we employ the Jensen-Shannon and the
Gromov-Wasserstein losses respectively between the SR-HR and LR-SR pairs of
distributions to further aid the generator of SuRGe to better exploit the
available information in an attempt to improve SR. Moreover, we train the
discriminator of SuRGe with the Wasserstein loss with gradient penalty, to
primarily prevent mode collapse. The proposed SuRGe, as an end-to-end GAN
workflow tailor-made for super-resolution, offers improved performance while
maintaining low inference time. The efficacy of SuRGe is substantiated by its
superior performance compared to 18 state-of-the-art contenders on 10 benchmark
datasets.