DMCM: Dwo-branch multilevel feature fusion with cross-attention mechanism for infrared and visible image fusion
Xicheng Sun, Fu Lv, Yongan Feng, Xu Zhang
TL;DR
This paper introduces a new image fusion method that combines infrared and visible images more effectively by improving feature extraction and reducing computational complexity.
Contribution
The novel two-branch feature interaction method with lightweight cross-attention fusion improves multi-scale information interaction and preserves texture details.
Findings
The proposed method outperforms existing algorithms in both subjective and objective evaluations on public datasets.
The algorithm achieves high operational efficiency and strong target detection performance on the M3FD dataset.
Abstract
In response to the limitations of current infrared and visible light image fusion algorithms—namely insufficient feature extraction, loss of detailed texture information, underutilization of differential and shared information, and the high number of model parameters—this paper proposes a novel multi-scale infrared and visible image fusion method with two-branch feature interaction. The proposed method introduces a lightweight multi-scale group convolution, based on GS convolution, which enhances multi-scale information interaction while reducing network parameters by incorporating group convolution and stacking multiple small convolutional kernels. Furthermore, the multi-level attention module is improved by integrating edge-enhanced branches and depthwise separable convolutions to preserve detailed texture information. Additionally, a lightweight cross-attention fusion module is…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Infrared Target Detection Methodologies · Remote-Sensing Image Classification