DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial Networks

Single Image Super-Resolution (SISR) has proven itself as a highly challenging and ill-posed problem. Multiple methods have been applied to this problem in the past, with varying degrees of success. Recently, methods using deep learning such as Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE) in particular have proven to be extremely effective. However, most of the present methods either create a blurry output, lacking fine details, or use extremely heavy models to achieve better results. We introduce a novel, lightweight GAN architecture for 4× super-resolution of images, which builds on previous methods, showing high quality of features both qualitatively and quantitatively. To achieve this, we use dilated convolutions in our generator architecture, a Markovian discriminator, a modified loss function and a training process more typical of a conditional GAN (cGAN). For testing our results qualitatively, we use Mean Opinion Score (MOS). The obtained MOS show the effectiveness of our model at generating visually superior images. Our code is available at https://www.github.com/kushalchordiya216/Super-Resolution.