Multi-window visual saliency extraction for fusion of visible and infrared images |
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| Affiliation: | 1. Institute of Electronic and Information, Hangzhou Dianzi University, Hangzhou, China;2. State Key Lab of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China;3. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China;1. Department of Information Engineering, Engineering University of Armed Police Force, Xi’an 710086, China;2. Department of Electronic Technology, Engineering University of Armed Police Force, Xi’an 710086, China;1. Yunnan University, School of Information, Kunming 650091, China;2. Yunnan University, School of Software, Kunming 650091, China;3. National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, China;1. School of Automation, Beijing Institute of Technology, Beijing 100081, China;2. Beijing Aerospace Automatic Control Institute, 100854 Beijing, China;1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China;2. Air Force Early Warning Academy, Wuhan, China |
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| Abstract: | Fusion for visible and infrared images aims to combine the source images of the same scene into a single image with more feature information and better visual performance. In this paper, the authors propose a fusion method based on multi-window visual saliency extraction for visible and infrared images. To extract feature information from infrared and visible images, we design local-window-based frequency-tuned method. With this idea, visual saliency maps are calculated for variable feature information under different local window. These maps show the weights of people’s attention upon images for each pixel and region. Enhanced fusion is done using simple weight combination way. Compared with the classical and state-of-the-art approaches, the experimental results demonstrate the proposed approach runs efficiently and performs better than other methods, especially in visual performance and details enhancement. |
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| Keywords: | Image fusion Visual saliency extraction Multi-window Dual-band Human visual system |
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