人工智能
图像融合
计算机科学
计算机视觉
特征(语言学)
保险丝(电气)
模式识别(心理学)
特征提取
复合图像滤波器
投影(关系代数)
融合
图像(数学)
算法
物理
哲学
语言学
量子力学
作者
Yu Tian,Wenjing Yang,Ji Wang
出处
期刊:Applied Optics
[The Optical Society]
日期:2021-07-19
卷期号:60 (24): 7466-7466
被引量:15
摘要
In recent years, image fusion has emerged as an important research field due to its various applications. Images acquired by different sensors have significant differences in feature representation due to the different imaging principles. Taking visible and infrared image fusion as an example, visible images contain abundant texture details with high spatial resolution. In contrast, infrared images can obtain clear target contour information according to the principle of thermal radiation, and work well in all day/night and all weather conditions. Most existing methods employ the same feature extraction algorithm to get the feature information from visible and infrared images, ignoring the differences among these images. Thus, this paper proposes what we believe to be a novel fusion method based on a multi-level image decomposition method and deep learning fusion strategy for multi-type images. In image decomposition, we not only utilize a multi-level extended approximate low-rank projection matrix learning decomposition method to extract salient feature information from both visible and infrared images, but also apply a multi-level guide filter decomposition method to obtain texture information in visible images. In image fusion, a novel fusion strategy based on a pretrained ResNet50 network is presented to fuse multi-level feature information from both visible and infrared images into corresponding multi-level fused feature information, so as to improve the quality of the final fused image. The proposed method is evaluated subjectively and objectively in a large number of experiments. The experimental results demonstrate that the proposed method exhibits better fusion performance than other existing methods.
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