基于生成对抗网络的红外与可见光图像融合算法

Wencheng Zhuang, Xiaona Tang, Binquan Zhang, Guangming Yuan
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引用次数: 0

摘要

可见光图像具有良好的轮廓和纹理信息,而红外图像具有全天候工作的优势。因此,对于夜间低照度目标的检测和分析,可以将可见光和红外图像信息融合,提高探测系统对夜间目标的检测精度和抗干扰能力。本文提出了一种基于生成式对抗网络的红外与可见光图像融合算法,该算法通过对两个鉴别器和生成器的对抗训练,有效提取红外与可见光图像的特征信息,通过引入注意机制和结构相似度损失函数,提高融合图像的特征提取能力和质量,并通过TTUR增强网络训练的稳定性。实验结果表明,本文算法在主观和客观评价方面都优于其他典型算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Infrared and visible image fusion algorithms based on generative adversarial networks
Visible images have good contour and texture information, while infrared images have the advantage of working in all weather. Therefore, for the detection and analysis of targets in low illumination at night, the information from visible and infrared images can be fused to improve the detection accuracy and anti-interference capability of detection systems for nighttime targets. In this paper, we propose a generative adversarial network-based fusion algorithm for IR and visible images, which can effectively extract the feature information of IR and visible images by adversarial training of two discriminators and generators, improve the feature extraction ability and the quality of fused images by introducing attention mechanism and structural similarity loss function, and enhance the stability of network training by TTUR. The experimental results show that the algorithm in this paper outperforms other typical algorithms in both subjective and objective evaluations.
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