  Recently, most of state-of-the-art single image super-resolution (SISR)
methods have attained impressive performance by using deep convolutional neural
networks (DCNNs). The existing SR methods have limited performance due to a
fixed degradation settings, i.e. usually a bicubic downscaling of
low-resolution (LR) image. However, in real-world settings, the LR degradation
process is unknown which can be bicubic LR, bilinear LR, nearest-neighbor LR,
or real LR. Therefore, most SR methods are ineffective and inefficient in
handling more than one degradation settings within a single network. To handle
the multiple degradation, i.e. refers to multi-domain image super-resolution,
we propose a deep Super-Resolution Residual StarGAN (SR2*GAN), a novel and
scalable approach that super-resolves the LR images for the multiple LR domains
using only a single model. The proposed scheme is trained in a StarGAN like
network topology with a single generator and discriminator networks. We
demonstrate the effectiveness of our proposed approach in quantitative and
qualitative experiments compared to other state-of-the-art methods.
