详情

原文传递 Infrared and visible images fusion by using sparse representation and guided filter

题名：	Infrared and visible images fusion by using sparse representation and guided filter
正文语种：	英文
作者：	Qilei Li;Wei Wu;Lu Lu;Zuoyong Li;Awais Ahmad;Gwanggil Jeon
作者单位：	Sichuan University;Minjiang University;Bahria University;Incheon National University
关键词：	Guided filter (GF); image fusion; sparse representation (SR);weight map
摘要：	Infrared and visible images play an importa nt role in transportation systems since they can monitor traffic conditions around the clock. However, visible images are susceptible to the imaging environments, and infrared images are not rich eno ugh in detail. The infrared and visible images fusion techniques can fuse these two different modal images into a single image with more useful information. In this paper, we propose an effective infrared and visible images fusion method for traffic systems. The weight maps are measured by utilizing the sparse coefficients. The next is to decompose the infrared and visible pair into high-frequency layers (HFLs) and low-frequency layers (LFLs). Since the two layers contain different structures and texture information, to extract the representative component, the guided filter is utilized to optimize weight maps in accordanee with the different characteristic of the infrared and visible pairs. The final step is to reconstruct the two-scale layers according to the weight maps. Experimental results demonstrate our method outperforms other popular approaches in terms of subjective perception and objective metrics.
出版日期：	2020
出版年：	2020
期刊名称：	Journal of Intelligent Transportation Systems Technology Planning and Operations
卷：	Vol24
期：	No01-06
页码：	254-263