Kernel based color estimation for night vision imagery |
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| Authors: | Xiaojing Gu Shaoyuan Sun Jian'an Fang Peng Zhou |
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| Affiliation: | 1. Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China;2. Department of Automation, College of Information Science and Technology, Donghua University, Shanghai, China;3. Department of Computing, The Hong Kong Polytechnic University, Hunghom, Hong Kong;1. School of Electronic and Optical Engineering, Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense (SIIS), Nanjing University of Science and Technology, Nanjing 210094, China;2. Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, 1065 av. de la Médecine, Quebec City, QC G1V 0A6, Canada;3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China;1. State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, 2699 Qianjin Street, Changchun 130012, China;2. International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science(NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan;3. Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan;1. College of Chemistry and Materials Science, Anhui Normal University, Wuhu 241000, PR China;2. Mechanical Engineering Department, Anhui Polytechnic University, Wuhu 241000, PR China;3. Research Center for Biomimetic Functional Materials and Sensing Devices, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, PR China;1. School of Electronic Science and Technology, Institute for Sensing Technologies, Key Laboratory of Liaoning for Integrated Circuits Technology, Dalian University of Technology, Dalian 116024, China;2. Center for Industrial Sensors and Measurements (CISM), Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA |
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| Abstract: | Displaying night vision (NV) imagery with colors can largely improve observer's performance of scene recognition and situational awareness comparing to the conventional monochrome representation. However, estimating colors for single-band NV imagery has two challenges: deriving an appropriate color mapping model and extracting sufficient image features required by the model. To address these, a kernel based regression model and a set of multi-scale image features are used here. The proposed method can automatically render single-band NV imagery with natural colors, even when it has abnormal luminance distribution and lacks identifiable details. |
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