Hybrid-Domain Attention Dense Network for Efficient Image Super-Resolution

Efficient Image Super Resolution (EISR) techniques are critical to meeting the real-time demands of resource-constrained devices. Current approaches focus too much on parameter reduction, thus slightly sacrificing image quality. To alleviate this issue, we propose a novel Hybrid Attention Dense Network (HADN) in this paper. HADN fully explores how to balance model performance and image recovery quality better. It incorporates two key designs: (1) We apply Hybrid-domain Attention Dense Blocks (HADB) with different feature layers cumulatively connected to enhance the representation of shallow features. (2) We designed a novel Hybrid-domain Attention Block (HAB) to reinforce the correlation between image edges and receptive fields for effective information fusion to improve the perception of detailed image features. Extensive experimental analyses affirm the competitiveness of the proposed method, showcasing its ability to balance performance and recovery effectively, even with smaller training datasets. The code is available at https://github.com/Yuii666/HADN .