计算机科学
光学
人工智能
红外线的
卷积神经网络
极化(电化学)
模式识别(心理学)
计算机视觉
特征提取
融合
图像融合
物理
图像(数学)
哲学
物理化学
语言学
化学
作者
Kunyuan Li,Meibin Qi,Shuo Zhuang,Yanfang Yang,Jun Gao
出处
期刊:Optics Letters
[Optica Publishing Group]
日期:2022-08-01
卷期号:47 (16): 4255-4255
被引量:18
摘要
The fusion of infrared intensity and polarization images can generate a single image with better visible perception and more vital information. Existing fusion methods based on a convolutional neural network (CNN), with local feature extraction, have the limitation of fully exploiting salient target features of polarization. In this Letter, we propose a transformer-based deep network to improve the performance of infrared polarization image fusion. Compared with existing CNN-based methods, our model can encode long-range features of infrared polarization images to obtain global contextual information using the self-attention mechanism. We also design a loss function with the self-supervised constraint to boost the performance of fusion. Experiments on the public infrared polarization dataset validate the effectiveness of the proposed method. Our approach achieves better fusion performance than the state-of-the-art.
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