人工智能
计算机科学
融合
图像融合
极化(电化学)
模式识别(心理学)
计算机视觉
拉普拉斯变换
图像(数学)
数学
哲学
数学分析
语言学
化学
物理化学
作者
Chenguang Wang,Ruyue Ma,Deli Yan,Huiliang Cao,Chong Shen
出处
期刊:Physica Scripta
[IOP Publishing]
日期:2024-01-03
卷期号:99 (2): 026003-026003
被引量:2
标识
DOI:10.1088/1402-4896/ad1a2f
摘要
Abstract Because of their complementary characteristics, intensity images and polarization images are often fused to produce information-rich images. However, the polarization characteristics are easily affected by the object’s environment, and the image fusion process may lose important information. In this paper, we propose an unsupervised end-to-end network framework based on a CNN for intensity images and degree of linear polarization images. First, we construct our own polarization dataset to solve the limitations of the training dataset; a hybrid loss function is designed to form an unsupervised learning process; and a Laplace operator enhancement layer is introduced into the network to further improve the quality of the fused images. Subjective and objective comparison experiments prove that the proposed fusion network is visually superior to several classical fusion methods.
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