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Design of High-Performance Automatic White Balance Algorithms with Inherent Fuzzy Inference and Neural Networks for Digital Image Capture Devices
|關鍵字:||White balance;白平衡;fuzzy system;single objective optimization;fuzzy neural network;模糊系統;單一目標最佳化方法;模糊類神經網路||出版社:||電機工程學系所||引用:|| J. Chiang, “Gray World Assumption,” Psych 221/EE 362 course project, Department of Psychology, Stanford University, U.S.A., 1999.  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, no.3, pp.428-434, 2002.  H.-K. Lam, O. C. Au, and C.-W. Wong, “Automatic white balancing using standard deviation of RGB components,” Proceedings - IEEE International Symposium on Circuits and Systems, v3, 2004 IEEE International Symposium on Circuits and Systems - Proceedings; Volume III of V: Cellular Neural Networks and Array Computing, Digital Signal Processing, Nanoelectronics and Gigascale Systems, pp.921-924, 2004.  H.-K. Lam, O. C. Au, and C.-W. Wong, “Automatic white balancing using luminance component and standard deviation of RGB components,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v3, Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.493-496, 2004.  H.-K. Lam, O. C. Au and C.-W. Wong, “Automatic white balancing using adjacent channels adjustment in RGB domain,” 2004 IEEE International Conference on Multimedia and Expo (ICME), v2, 2004 IEEE International Conference on Multimedia and Expo (ICME), pp.979-982, 2004.  J. Wang, Y. Liu, F. Liu, H. Xiong and C. Li, “A method of automatic white balance using fuzzy logic,” IEEE Asia-Pacific Conference on Circuits and Systems - Proceedings, 2000 IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems, pp.615-617, 2000.  Y.-C. Liu, W.-H. Chan and Y.-Q. Chen, “Automatic white balance for digital still camera,” IEEE Transactions on Consumer Electronics, vol.41, no.3, pp.460-466, 1995.  T. Haruki and K. Kikuchi, “Video camera system using fuzzy logic,” IEEE Transactions on Consumer Electronics, vol.38, no.3, pp.624-634, 1992.  C. Shumate and H. Li, “Perfect Reflector Assumption,” Psych 221/EE 362 course project, Department of Psychology, Stanford University, U.S.A., 2000.  P.-M. Wang and C.-S. Fuh, “Automatic white balance with color temperature estimation,” Digest of Technical Papers - IEEE International Conference on Consumer Electronics, Digest of Technical Papers - 2007 International Conference on Consumer Electronics, ICCE 2007, p 4146227, 2007.  W.-C. Kao, S.-H. Wang, C.-C. Kao, C.-W. Huang and S.-Y. Lin, “Color reproduction for digital imaging systems,” Proceedings - IEEE International Symposium on Circuits and Systems, ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings, pp.4599-4602, 2006.  W.-C. Kao, S.-H. Wang, L.-Y. Chen, and S.-Y. Lin, “Design considerations of color image processing pipeline for digital cameras,” IEEE Transactions on Consumer Electronics, vol.52, no.4, pp.1144-1152, 2006.  J.-Y. Huo, Y.-L. Chang, J. Wang and X.-X. Wei, “Robust automatic white balance algorithm using gray color points in images,” IEEE Transactions on Consumer Electronics, vol.52, no.2, pp.541-546, 2006.  N. Kehtarnavaz, H. J. Oh, and Y. Yoo, “Development and real-time implementation of auto white balancing scoring algorithm,” Real-Time Imaging, vol.8, no.5, pp.379-386, 2002.  H. Yamashina, K. Fukushima and H. Kano, “White balance in inspection systems with a neural network,” Computer Integrated Manufacturing Systems, vol.9, no.1, pp.3-8, 1996.  M. Abe, H. Ikeda, Y. Higaki and M. Nakamichi, “A method to estimate correlated color temperatures of illuminants using a color video camera,” IEEE Transactions on Instrumentation and Measurement, vol.40, no.1, pp.28-33, 1991.  M. Abe, H. Ikeda, Y. Higaki, M. Amano and M. Nakamichi, “Method to estimate correlated color temperature of illuminant using color video camera,” IEEE Instrum Meas Technol Conf, pp.19-23, 1989.  V. Chikane, and C.-S. Fuh, “Automatic white balance for digital still cameras,” Journal of Information Science and Engineering, vol.22, no.3, pp.497-509, 2006.  E. Y. Lam, 2005. “Combining gray world and retinex theory for automatic white balance in digital photography,” Proceedings of the International Symposium on Consumer Electronics, ISCE, Proceedings of the Ninth International Symposium on Consumer Electronics 2005, ISCE 2005, pp.134-139, 2005.  R. Lukac, “Refined automatic white balancing,” Electronics Letters, vol.43, no.8, pp.445-446, 2007.  S. Bianco, F. Gasparini, and R. Schettini, “Combining Strategies for White Balance,” Proceedings of SPIE - The International Society for Optical Engineering, v6502, Proceedings of SPIE-IS and T Electronic Imaging - Digital Photography III, p. 65020D, 2007.  L. Jinlong, “An automatic white balance method based on edge detection,” Proceedings of the International Symposium on Consumer Electronics, ISCE, 2006 IEEE Tenth International Symposium on Consumer Electronics, ISCE 2006 - Proceedings, pp.101-104, 2006.  C. Lu and M. S. Drew, “Automatic compensation for camera settings for images taken under different illuminants,” Final Program and Proceedings - IS and T/SID Color Imaging Conference, v2006, Fourteenth Color Imaging Conference: Color Science and Engineering Systems, Technologies, Applications - Final Program and Proceedings, pp.114-118, 2006.  K. Hirakawa and T. W. Parks, “Chromatic adaptation and white-balance problem,” Proceedings - International Conference on Image Processing, ICIP, v 3, IEEE International Conference on Image Processing 2005, ICIP 2005, pp. 984-987, 2005.  E.-S. Kim, S.-H. Lee, S.-W. Jang and K.-I. Sohng, “Adaptive colorimetric characterization of camera for the variation of white balance,” IEICE Transactions on Electronics, v E88-C, no.11, pp.2086-2089, 2005.  F., Gasparini and R., Schettini “Color balancing of digital photos using simple image statistics,” Pattern Recognition, vol.37, no.6, pp.1201-1217, 2004.  J.-S. Lee, Y.-Y. Jung, B.-S. Kim and S.-J. Ko, “An advanced video camera system with robust AF, AE, and AWB control,” IEEE Transactions on Consumer Electronics, vol.47, no.3, pp.694-699, 2001.  B. Hu, Q. Lin, X. Kang and G. Chen, “A new algorithm for automatic white balance with priori,” IEEE Asia-Pacific Conference on Circuits and Systems - Proceedings, 2000 IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems, pp.109-112, 2000.  D. Qian, J. Toker and S. Bencuya, “Automatic light spectrum compensation method for CCD white balance measurement,” IEEE Transactions on Consumer Electronics, vol.43, no.2, pp.216-220, 1997.  P.-M. Wang and C.-S. Fuh, "Automatic White Balance with Color Temperature Estimation," Master Thesis, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, 2006.  Danny Pascale, “A review of RGB color spaces.”  http://www.techmind.org/colour/coltemp.html  R. S. Berns, Billmeyer and Saltzman''s Principles of Color Technology, Wiley-Interscience, New York, 2000.  Edwin K. P. Chong and Stanislaw H. Zak, An Introduction to Optimization, 2nd Ed., Wiley-Interscience, New York, 2001.  H.-N. Robert, Neurocomputing, Addison-Wesley, 1990.  C.-T. Lin and George C. S. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, 1996.  C.-F. Juang and C.-T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Transactions on Fuzzy Systems, vol.6, no.4, pp.12-32, 1998.  C.-F. Juang and C.-T. Lin, “Recurrent self-organizing neural fuzzy inference network,” IEEE Transactions on Neural Networks, vol.10, no.4, pp.828-845, 1999.||摘要:||
Auto white balance is an important process in digital camera or CCD camera. Without auto white balance, the image becomes reddish under low color temperature and becomes bluish under high color temperature. In this thesis, we propose optimization methods and light estimation method. The optimization methods formulate the auto white balance to three optimization problems and solve them. The light estimation method use two steps: (1) utilize fuzzy neural network to estimate the color temperature of an image; (2) apply scale factors to adjust the image. Our methods have good performance in the experiment.
|Appears in Collections:||電機工程學系所|
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