CAAT: Image super-resolution algorithm via channel attention and transformer

Abstract

Deep learning-based single image super-resolution (SISR) has achieved remarkable progress, yet the trade-off between reconstruction quality and computational efficiency remains a critical challenge for real-time applications. This paper proposes a novel Channel Attention and Transformer framework (CAAT) that synergistically integrates convolutional operations with Swin Transformer blocks to achieve lightweight yet high-performance SR. The core innovation lies in the Channel-Attention-Embedded Transformer Block, which adaptively injects channel attention mechanisms into both Transformer self-attention and convolutional feature streams, enabling discriminative feature selection and cross-modal fusion at the block level. By alternately stacking convolution and Transformer layers with channel-wise adaptive weighting, proposed leverages their complementary strengths in local detail preservation and global context modeling while maintaining model compactness. Extensive evaluations on five benchmark datasets across three scales demonstrate that proposed achieves superior performance over six state-of-the-art methods. Notably, at × 4 magnification, proposed attains 0.09 dB PSNR improvement on Urban100 and 0.30 dB on Manga109 compared to the best counterparts, while reducing parameters by 51 % versus SwinIR and FLOPs by 68 % (195.6G vs. 612.6G for 1280 × 720 input). These results, substantiated by statistical significance tests and ablation studies, confirm proposed efficacy as a cost-effective solution for real-time SR deployments.

Topics & Concepts