  Image super-resolution generation aims to generate a high-resolution image
from its low-resolution image. However, more complex neural networks bring high
computational costs and memory storage. It is still an active area for offering
the promise of overcoming resolution limitations in many applications. In
recent years, transformers have made significant progress in computer vision
tasks as their robust self-attention mechanism. However, recent works on the
transformer for image super-resolution also contain convolution operations. We
propose a patch translator for image super-resolution (PTSR) to address this
problem. The proposed PTSR is a transformer-based GAN network with no
convolution operation. We introduce a novel patch translator module for
regenerating the improved patches utilising multi-head attention, which is
further utilised by the generator to generate the 2x and 4x super-resolution
images. The experiments are performed using benchmark datasets, including
DIV2K, Set5, Set14, and BSD100. The results of the proposed model is improved
on an average for $4\times$ super-resolution by 21.66% in PNSR score and 11.59%
in SSIM score, as compared to the best competitive models. We also analyse the
proposed loss and saliency map to show the effectiveness of the proposed
method.
