PTSR: Patch Translator for Image Super-Resolution
This work addresses efficiency and performance limitations in image super-resolution for applications like photography and medical imaging, though it is incremental as it builds on existing transformer and GAN methods.
The paper tackles the problem of high computational costs in image super-resolution by proposing a transformer-based GAN network without convolution operations, achieving average improvements of 21.66% in PSNR and 11.59% in SSIM for 4x super-resolution compared to competitive models.
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.