  Image super-resolution aims to synthesize high-resolution image from a
low-resolution image. It is an active area to overcome the resolution
limitations in several applications like low-resolution object-recognition,
medical image enhancement, etc. The generative adversarial network (GAN) based
methods have been the state-of-the-art for image super-resolution by utilizing
the convolutional neural networks (CNNs) based generator and discriminator
networks. However, the CNNs are not able to exploit the global information very
effectively in contrast to the transformers, which are the recent breakthrough
in deep learning by exploiting the self-attention mechanism. Motivated from the
success of transformers in language and vision applications, we propose a
SRTransGAN for image super-resolution using transformer based GAN.
Specifically, we propose a novel transformer-based encoder-decoder network as a
generator to generate 2x images and 4x images. We design the discriminator
network using vision transformer which uses the image as sequence of patches
and hence useful for binary classification between synthesized and real
high-resolution images. The proposed SRTransGAN outperforms the existing
methods by 4.38 % on an average of PSNR and SSIM scores. We also analyze the
saliency map to understand the learning ability of the proposed method.
