UASR: An Unified-Attention Mixer Network for Efficient Image Super-Resolution

Recent works in single-image super-resolution (SISR) have brought notable improvements to the field. Transformer-based methods enhance reconstruction quality by capturing long-range dependencies. However, the quadratic computational complexity of multi-head self-attention (MHSA) introduces efficiency bottlenecks in HR image processing, and insufficient local feature extraction limits the recovery of fine texture details and edge sharpness. In contrast, convolutional neural network (CNN)-based methods suffer from limited receptive fields, leading to inadequate high-frequency detail recovery and blurring artifacts. Generally, Transformer-based and CNN-based methods fail to simultaneously address the challenges of computational efficiency, global dependency modeling, and local feature extraction. To integrate the strengths of both paradigms, we propose Unified-Attention Super-Resolution(UASR) network, a lightweight architecture based on the Convolutional Transformer(ConvFormer) layer. Specifically, UASR replaces MHSA with the Unified-Attention Mixer (UA-M) that efficiently captures global dependencies at a low computational cost. Additionally, the Reparameterized Edge-Extraction FeedForward Network (REFN) supplements UA-M by focusing on extracting texture and edge features. Furthermore, we introduce a Spectral Unified-Attention Block (SUAB) that extends the capabilities of UA-M into the frequency domain, thus improving detail reconstruction and accelerating the computation process. Compared to current CNN-based and Transformer-based SISR models, experimental results demonstrate that our method strikes an effective balance between accuracy and efficiency, enhancing texture fidelity and super-resolution performance.