Open AccessFusion of Infrared and Visible Images Based on Infrared Object Extraction
Author(s) -
Chuanzhen Rong,
Gaohang Liu,
Zhuolin Ping,
Yongxing Jia,
Zhenjun Yue,
Guanghui Xu
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.11.013
Subject(s) - infrared , artificial intelligence , computer vision , computer science , segmentation , scale (ratio) , pattern recognition (psychology) , object (grammar) , image fusion , enhanced data rates for gsm evolution , fusion , image (mathematics) , optics , physics , linguistics , philosophy , quantum mechanics
The ideal fused results of infrared and visible images, should contain the important infrared objects, and preserve the visible textural detail information as much as possible. The fused images are more consistent with human visual perception effect. For this purpose, a novel infrared and visible image fusion framework is proposed. Under the guidance of the model, the source images are decomposed into large‐scale edge, small‐scale textural detail and coarse‐scale base level information. Among which, the large‐scale edge information contains the main infrared features, on this basis, the infrared image is further segmented into the object, transition and background regions by OTSU multi‐threshold segmentation algorithm. In the end, the fused weights for the decomposed sub‐information are determined by the segmented results, so that, the infrared object information can be effectively injected into the fused image, and the important visible textural detail information can be preserved as much as possible in the fused image. Experimental results show that, the proposed method can not only highlight the infrared objects, but also preserve the visual information in the visible image as much as possible. The fused results are superior to the commonly used representative fusion methods, both in subjective perception and objective evaluation.