Genetic algorithm based deep multi-route self-attention for single image super-resolution

Abstract

Image restoration, such as single image super-resolution (SISR), is a long-established low-level vision issue that intends to regenerate high-resolution (HR) images from low-resolution (LR) input counterparts. While state-of-the-art image super-resolution models are based on the well-known convolutional neural network (CNN), many self-attention-based or transformer-based experiment attempts have been conducted. They have shown promising performance on vision problems. A powerful baseline model based on the swin transformer adopts the shifted window approach. It enhances the capability by restricting the model to compute the self-attention function only on non-superimpose local windows while enabling cross-window relations. However, the architecture design is manually fixed. Therefore, the results are not achieving optimal performance. This work presents a genetic algorithm-based deep multi-route self-attention network for single image super-resolution (GA-MRSA). The genetic algorithm (GA) is introduced to discover the more suitable number of filters and layers. Experimental results demonstrate that the proposed optimization technique can produce an SR image with a maximum progressive PSNR of 0.14 dB and an average of 0.06 dB in the testing datasets compared to the state-of-the-art.