Infrared and visible image fusion based on multi-scale dense attention connection network

Yong CHEN; Jiaojiao ZHANG; Zhen WANG

doi:10.37188/OPE.20223018.2253

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Infrared and visible image fusion based on multi-scale dense attention connection network

Information Sciences | 更新时间：2022-09-26

- Infrared and visible image fusion based on multi-scale dense attention connection network
- Optics and Precision Engineering Vol. 30, Issue 18, Pages: 2253-2266(2022)
- 作者机构：
  
  兰州交通大学电子与信息工程学院，甘肃兰州 730070
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223018.2253
  CLC： TP391
- Received：17 February 2022，
  
  Revised：20 March 2022，
  
  Published：25 September 2022
- 稿件说明：
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陈永,张娇娇,王镇.多尺度密集连接注意力的红外与可见光图像融合[J].光学精密工程,2022,30(18):2253-2266.

CHEN Yong,ZHANG Jiaojiao,WANG Zhen.Infrared and visible image fusion based on multi-scale dense attention connection network[J].Optics and Precision Engineering,2022,30(18):2253-2266.
陈永,张娇娇,王镇.多尺度密集连接注意力的红外与可见光图像融合[J].光学精密工程,2022,30(18):2253-2266. DOI： 10.37188/OPE.20223018.2253.

CHEN Yong,ZHANG Jiaojiao,WANG Zhen.Infrared and visible image fusion based on multi-scale dense attention connection network[J].Optics and Precision Engineering,2022,30(18):2253-2266. DOI： 10.37188/OPE.20223018.2253.

摘要

针对现有红外与可见光图像融合时，融合结果存在细节信息丢失、特征提取不足等问题，提出了一种多尺度密集连接注意力的红外与可见光图像融合深度学习网络模型。首先，设计多尺度卷积提取红外与可见光图像中不同尺度信息，增大感受野特征提取范围，克服了单一尺度特征提取不足的问题。然后，通过密集连接网络增强特征提取，并在编码子网络末端采用提出的可变形卷积注意力机制，密切联系全局上下文信息，增强对红外与可见光图像中重要特征信息的聚焦能力。最后，由全卷积层构成解码网络，重构生成融合图像。本文选取了六种图像融合客观评价指标，红外与可见光图像公开数据集融合实验结果表明：与其他8种方法相比，本文算法对比实验指标均有所提高，其中结构相似性（SSIM）、空间频率（SF）指标分别平均提高了0.26倍、0.45倍。所提方法的融合结果保留了更清晰的边缘及目标信息，具有更好的对比度和清晰度，在客观评价方面均优于对比方法。

Abstract

To solve the loss of detail information and insufficient feature extraction in the fusion results of infrared and visible light images， a deep learning network model for infrared and visible light image fusion with multi-scale densely connected attention is proposed. First， multi-scale convolution is designed to extract information of different scales in infrared and visible light images to increase the feature extraction range in the receptive field and overcome the problem of insufficient feature extraction at a single scale. Then， feature extraction is enhanced through a densely connected network， and an attention mechanism is introduced at the end of the encoding sub-network to closely connect the global context information and enhance the ability to focus on important feature information in infrared and visible light images. Finally， the fully convolutional layers that compose the decoding network are used to reconstruct the fused image. This study selects six objective evaluation indicators of image fusion， and the fusion experiments conducted on public infrared and visible light image datasets show that the proposed algorithm exhibits improved results compared with eight other methods. The structural similarity （SSIM）， spatial frequency （SF） indicators increase by an average of 0.26 and 0.45 times， respectively. The fusion results of the proposed method retain clearer edge and target information with better contrast and clarity， and are superior to the compared methods in both subjective and objective evaluations.

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