Image super-resolution is a critical task that aims to synthesize high-resolution images from low-resolution images, addressing limitations in applications such as low-resolution object recognition and medical image enhancement. Generative adversarial network (GAN) based methods have been the state-of-the-art in image super-resolution, utilising convolutional neural networks (CNNs) for both generator and discriminator networks. However, CNNs are not optimal for exploiting global information, unlike transformers, a recent breakthrough in deep learning due to their self-attention mechanism. Inspired by the success of transformers in language and vision applications, we propose a novel image super-resolution method called SRTransGAN, which leverages a transformer-based GAN architecture. Our approach employs a transformer-based encoder-decoder network as a generator to produce 2 \(\times \) and 4 \(\times \) high-resolution images. Our proposed SRTransGAN outperforms existing methods by 4.38% on average, as measured by PSNR and SSIM scores. We also demonstrate the effectiveness of our method through saliency map analysis. Our method provides a promising new approach for image super-resolution, highlighting the potential of transformer-based architectures in addressing this critical problem. The code used in the paper is publicly available at https://github.com/nbaghel777/SRTransGAN .