DFA-Net: Multi-Scale Dense Feature-Aware Network via Integrated Attention for Unmanned Aerial Vehicle Infrared and Visible Image Fusion

Author:

Shen Sen1,Li Di2,Mei Liye2,Xu Chuan2,Ye Zhaoyi2ORCID,Zhang Qi2,Hong Bo3,Yang Wei3ORCID,Wang Ying3

Affiliation:

1. School of Weapon Engineering, Naval Engineering University, Wuhan 430032, China

2. School of Computer Science, Hubei University of Technology, Wuhan 430068, China

3. School of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China

Abstract

Fusing infrared and visible images taken by an unmanned aerial vehicle (UAV) is a challenging task, since infrared images distinguish the target from the background by the difference in infrared radiation, while the low resolution also produces a less pronounced effect. Conversely, the visible light spectrum has a high spatial resolution and rich texture; however, it is easily affected by harsh weather conditions like low light. Therefore, the fusion of infrared and visible light has the potential to provide complementary advantages. In this paper, we propose a multi-scale dense feature-aware network via integrated attention for infrared and visible image fusion, namely DFA-Net. Firstly, we construct a dual-channel encoder to extract the deep features of infrared and visible images. Secondly, we adopt a nested decoder to adequately integrate the features of various scales of the encoder so as to realize the multi-scale feature representation of visible image detail texture and infrared image salient target. Then, we present a feature-aware network via integrated attention to further fuse the feature information of different scales, which can focus on specific advantage features of infrared and visible images. Finally, we use unsupervised gradient estimation and intensity loss to learn significant fusion features of infrared and visible images. In addition, our proposed DFA-Net approach addresses the challenges of fusing infrared and visible images captured by a UAV. The results show that DFA-Net achieved excellent image fusion performance in nine quantitative evaluation indexes under a low-light environment.

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

Reference59 articles.

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