基于四路多尺度注意力模块及桥连结构的轻量级单图超分辨率网络

doi:10.16451/j.cnki.issn1003-6059.202511004

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Abstract

A²F-SD, the image super-resolution network, is built on squeeze and excitation attention module. In this paper, the limitations of A²F-SD are analyzed, including the excessively simplistic attention mechanism and insufficient utilization of multi-path information. To address these issues, a lightweight single-image super-resolution network based on four-path multi-scale attention module and bridging structure(A²F-MSAB) is proposed to optimize the attention modules and hourglass structure in the original A²F-SD model. First, multi-scale self-learning average-variance attention modules are designed. A four-path attention module is employed to enhance the image feature extraction ability of the network. Then, the hourglass structure of A²F-SD is improved into the bridge and multi-level hourglass structure to facilitate information flow between different modules, and between shallow and deep layers of the model, thereby strengthening the model reconstruction ability for medium and high-frequency details. Experiments show that A²F-MSAB model with only half of the basic modules stacked achieves superior performance and its evaluation metrics on certain datasets outperform those of both A²F-SD and A²F-S.

Key words： Image Super-Resolution Attention Mechanism Feature Fusion Deep Learning

Received: 23 September 2025

ZTFLH:

TP391

Fund:

Supported by Shenzhen Science and Technology Program(No.KJZD20230923114600002)

Corresponding Authors: XU Yong, Ph.D., professor. His research interests include pa-ttern recognition, biometric recognition, image processing, video analysis, and machine learning.

About author:: SU Bohejun, Master student. His research interests include computer vision.
XUE Rui, Master, senior experimenter. Her research interests include computer systems and computer vision.
LIU Wei, Master, lecturer. His research interests include computer vision.
WANG Haoqian, Ph.D., professor. His research interests include computational photography and artificial intelligence.

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Cite this article:

SU Bohejun,XU Yong,XUE Rui等. Lightweight Single-Image Super-Resolution Network Based on Four-Path Multi-scale Attention Module and Bridging Structure[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(11): 999-1012.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202511004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I11/999

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