Work

Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution

Year: 2025

Type: article

Abstract: In the field of remote sensing, super-resolution methods based on deep learning have made significant progress. However, redundant feature extraction and inefficient feature fusion can, respectively, result in excessive parameters and restrict the precise reconstruction of features, making the model difficult to deploy in practical remote-sensing tasks. To address this issue, we propose a lightweight Dual Attention Fusion Enhancement Network (DAFEN) for remote-sensing image super-resolution. Firstly, we design a lightweight Channel-Spatial Lattice Block (CSLB), which consists of Group Residual Shuffle Blocks (GRSB) and a Channel-Spatial Attention Interaction Module (CSAIM). The GRSB improves the efficiency of redundant convolution operations, while the CSAIM enhances interactive learning. Secondly, to achieve superior feature fusion and enhancement, we design a Forward Fusion Enhancement Module (FFEM). Through the forward fusion strategy, more high-level feature details are retained for better adaptation to remote-sensing tasks. In addition, the fused features are further refined and rescaled by Self-Calibrated Group Convolution (SCGC) and Contrast-aware Channel Attention (CCA), respectively. Extensive experiments demonstrate that DAFEN achieves better or comparable performance compared with state-of-the-art lightweight super-resolution models while reducing complexity by approximately 10∼48%.

Source: Remote Sensing

DOI: 10.3390/rs17061078

Authors: Wangyou Chen, Shenming Qu, Laigan Luo, Yongyong Lu

Institution: Henan University

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: National Natural Science Foundation of China