A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization |
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| Affiliation: | 1. School of Computer Science & Engineering, Xi’an University of Technology, Xi’an 710048, China;2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an 710048, China;1. Department of Automation, University of Science and Technology of China, Hefei 230026, China;2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;1. College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610064, PR China;2. School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan 610064, PR China;1. Department of Information Engineering, Engineering University of Armed Police Force, Xi’an 710086, China;2. Department of Electronics Technology, Engineering University of Armed Police Force, Xi’an 710086, China;1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;2. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China |
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| Abstract: | This paper proposes a novel image fusion scheme based on contrast pyramid (CP) with teaching learning based optimization (TLBO) for visible and infrared images under different spectrum of complicated scene. Firstly, CP decomposition is employed into every level of each original image. Then, we introduce TLBO to optimizing fusion coefficients, which will be changed under teaching phase and learner phase of TLBO, so that the weighted coefficients can be automatically adjusted according to fitness function, namely the evaluation standards of image quality. At last, obtain fusion results by the inverse transformation of CP. Compared with existing methods, experimental results show that our method is effective and the fused images are more suitable for further human visual or machine perception. |
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| Keywords: | Image fusion TLBO Contrast pyramid Optimization |
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