TSSA-Net: Transposed Sparse Self-Attention-based network for image super-resolution

Image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution ones. Transformer-based methods have recently demonstrated remarkable results, but the conventional dense self-attention mechanism fails to capture local relationships between patches, leading to suboptimal performance. Moreover, the recovery of high-frequency information, crucial for edge reconstruction, is often insufficient. To address these challenges, we propose the Transposed Sparse Self-Attention (TSSA) mechanism, which improves local feature attention by restructuring the self-attention computation, without using convolutions. Additionally, we introduce a Segmented Convolutional Feed-Forward Network (SCFFN) to enhance high-frequency detail recovery and local feature acquisition, while maintaining a low parameter count. We combine TSSA, SCFFN, and a channel attention mechanism to develop TSSA-Net, an innovative network for image super-resolution. Comprehensive evaluations on classical, lightweight, and real-world SR tasks show that TSSA-Net outperforms recent methods on the Set14, Urban100, and Manga109 datasets, with improvements of 0.01–0.04 decibel (dB), 0.11–0.12 dB, and 0.01–0.08 dB, respectively. TSSA-Net achieves notable results in both metric-based and visual-based evaluations. The code is available at https://github.com/VMC-Lab-Chen/TSSA-Net.