# SDAM: A dual attention mechanism for high-quality fusion of infrared and visible images

## TL;DR

This paper introduces SDAM, a new method for combining infrared and visible images that improves quality by preserving important details from both.

## Contribution

The novel dual-attention mechanism (SDAM) enhances fusion quality by preserving texture and salient information from both image types.

## Key findings

- SDAM outperforms existing methods in preserving texture details and infrared target prominence.
- The optimized loss function improves brightness, contrast, and overall visual quality of fusion images.
- Ablation experiments and public dataset evaluations confirm SDAM's superior performance.

## Abstract

Image fusion of infrared and visible images to obtain high-quality fusion images with prominent infrared targets has important applications in various engineering fields. However, current fusion processes encounter problems such as unclear texture details and imbalanced infrared targets and texture detailed information, which lead to information loss. To address these issues, this paper proposes a method for infrared and visible image fusion based on a specific dual-attention mechanism (SDAM). This method employs an end-to-end network structure, which includes the design of channel attention and spatial attention mechanisms. Through these mechanisms, the method can fully exploit the texture details in the visible images while preserving the salient information in the infrared images. Additionally, an optimized loss function is designed to combine content loss, edge loss, and structure loss to achieve better fusion effects. This approach can fully utilize the texture detailed information of visible images and prominent information in infrared images, while maintaining better brightness and contrast, which improves the visual effect of fusion images. Through conducted ablation experiments and comparative evaluations on public datasets, our research findings demonstrate that the SDAM method exhibits superior performance in both subjective and objective assessments compared to the current state-of-the-art fusion methods.