图像融合
计算机视觉
计算机科学
人工智能
图像(数学)
任务(项目管理)
融合
点(几何)
对偶(语法数字)
数学
工程类
艺术
语言学
哲学
几何学
文学类
系统工程
作者
Bokun Liu,Junyu Wei,Shaojing Su,Xiaozhong Tong
标识
DOI:10.1109/icivc55077.2022.9886778
摘要
In low light situations, a single visible image can not transmit reliable information, even cause the loss of the target information. At this point, the advantages of visible and infrared image fusion will be highlighted. For a given pair of visible and infrared images, they are collectively referred to as dual-light images in this paper. How to make the most of their information and improve the information expression ability of the fused image is crucial. The traditional evaluation methods use statistical indicators, which is not associated with the upstream task. In this paper, the image fusion method driven by the target detection task is studied. Semantic loss is added to guide the dual-light image fusion. Moreover, through the visual enhancement module, the impact of adverse factors ( low light, etc. ) on the image is weakened, and the information expression level of the image is improved. Thus, the final image is more beneficial to target detection.
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