Towards trustworthy image super-resolution via symmetrical and recursive artificial neural network

Abstract

AI-assisted living environments by widely apply the image super-resolution technique to improve the clarity of visual inputs for devices like smart cameras and medical monitors. This increased resolution enables more accurate object recognition, facial identification, and health monitoring, contributing to a safer and more efficient assisted living experience. Although rapid progress has been achieved, most current methods suffer from huge computational costs due to the complex network structures. To address this problem, we propose a symmetrical and recursive transformer network (SRTNet) for efficient image super-resolution via integrating the symmetrical CNN (S-CNN) unit and improved recursive Transformer (IRT) unit. Specifically, the S-CNN unit is equipped with a designed local feature enhancement (LFE) module and a feature distillation attention in attention (FDAA) block to realize efficient feature extraction and utilization. The IRT unit is introduced to capture long-range dependencies and contextual information to guarantee that the reconstruction image preserves high-frequency texture details. Extensive experiments demonstrate that the proposed SRTNet achieves competitive performance regarding reconstruction quality and model complexity compared with the state-of-the-art methods. In the $\times 2$, $\times 3$, and $\times 4$ super-resolution tasks, SRTNet achieves the best performance on the BSD100, Set14, Set5, Manga109, and Urban100 datasets while maintaining low computational complexity.