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A Novel Color Balance Algorithm Based on Fuzzy Inference Rules With Application to Digital Camera Systems
|關鍵字:||色彩平衡;Color Balance;模糊推論;影像處理;Fuzzy Inference;Image Processing||出版社:||電機工程學系所||引用:||G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Qualitative Data and Formulae, Wiley, New York, 1982. Po-Min Wang and Chiou-Shann Fuh, "Automatic White Balance with Color Temperature Estimation," International Conference on Consumer Electronics, pp. 1-2, 2007. Y. Kim, J.-S. Lee, A. W. Morales, and S.-J. Ko, "A video camera system with enhanced zoom tracking and auto white balance," IEEE Transactions on Consumer Electronics, vol. 48, pp. 428-434, 2002. J. Chiang, "Gray World Assumption," Psych 221/EE 362 curse project, Department of Psychology, Stanford University, U.S.A., 1999. G. Buchsbaum, "A spatial processor model for object color perception," Journal of the Franklin Institute, vol. 310, no. 1, pp. 1-26, 1980. R. Gershon, A. D. Jepson, and J. K. 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Color balance algorithm has become an important part of image processing pipeline. Many high-class digital cameras have built-in sensors which can measure illuminant in real-time and make correction to the captured images. Before color balanced, images will appear to have been shifted towards one color or another, i.e., have color cast. A color balance algorithm is a class of image processing developed to remove color cast. Human eyes are sensitive to the neutral colors of the image. Most color balance algorithm is mainly aimed at these colors to adjust image. Color balance algorithms aiming at white color are known as white balance algorithms. White balance is widely used in electronics products such as digital cameras. The common problem of most existing methods is that scene contents and illuminants will affect the performance.
In this thesis, we propose a novel color balance algorithm based on fuzzy inference rules by basic concept of color and the experimental results. In this algorithm, we form color sample database with the images are captured in different illuminants at first. Second, we use clustering results of color sample database to form fuzzy inference system and optimize the system. Finally, we adjust the image in unknown illuminant to the image in standard illuminant with correction the fuzzy inference system forecast, and compare the result with existing methods.
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