基于差异双分支编码器的多阶段图像融合方法

doi:10.16451/j.cnki.issn1003-6059.202207008

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Abstract In the existing infrared and visible image fusion methods, the details of the fused image are lost seriously and the visual effect is poor. Aiming at the problems, a multi-stage image fusion method based on differential dual-branch encoder is proposed. The features of multi-modal images are extracted by two encoders with different network structures to enhance the diversity of features. A multi-stage fusion strategy is designed to achieve refined image fusion. Firstly, primary fusion is performed on the differential features extracted by the two encoding branches in the differential dual-branch encoder. Then, mid-level fusion on the saliency features of the multi-modal images is conducted in the fusion stage. Finally, the long-range lateral connections are adopted to transmit shallow features of the differential dual-branch encoder implemented to the decoder and guide the fusion process and the image reconstruction simultaneously. Experimental results show the proposed method enhances the detailed information of the fused images and achieves better performance in both visual effect and objective evaluation.

Key words： Image Fusion Infrared Image Visible Image Convolutional Neural Network

Received: 27 April 2022

ZTFLH:

TN 911.73

Fund:Supported by National Natural Science Foundation of China(No.62020106012,U1836218,61672265), The 111 Project of Ministry of Education of China(No.B12018)

Corresponding Authors: WU Xiaojun, Ph.D., professor. His research interests include artificial intelligence, pattern recognition and computer vision.

About author:: About Author:HONG Yulu, master student. Her research interests include image fusion and deep lear-ning. XU Tianyang, Ph.D., associate professor. His research interests include artificial intelligence,pattern recognition and computer vision.

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Cite this article:

HONG Yulu,WU Xiaojun,XU Tianyang. Multi-stage Image Fusion Method Based on Differential Dual-Branch Encoder[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(7): 661-670.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202207008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I7/661

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