高动态彩色图像显示
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摘要
本文研究了彩色图像的显示问题,分析了国内外该领域的研究现状,在动态范围压缩和色彩校正两个问题上,针对其核心技术(如调整映射曲线,Retinex,自动色彩校正等)提出了改进算法或是新算法。解决了图像显示中亮、暗区难于同时增强的问题;并且通过卷积计算解决了自动色彩校正技术的速度问题。试验效果良好,为彩色图像显示的实用化打下了基础。
第一章是绪论,主要介绍了动态图像显示和颜色校正两个问题产生的背景,特点和现有处理此问题的方法。并且给出了本文在这个方面的相关工作。并且稍后指出两者的关系。
第二章是调整映射技术,这里我们介绍了人眼视觉系统的特征,描述了基于此的两种主流算法,调整映射曲线和调整映射操作。并将人眼视觉系统和直方图结合起来,得到新的快速调整映射曲线算法。
第三章是Retinex技术,在简单的Retinex介绍之后,给出了两个有代表性的Retinex模型—MMT-99,MSR(Multi-Scale Retinex)。并且基于前者提出了两种改进算法,尤其是后面的改进是将Retinex技术嵌入到小波中,得到良好的效果。
第四章是色彩校正技术,在分析了色彩校正技术产生的背景和所面临的问题后,介绍了一些重要的校正算法,如GW(Gray World),ACE(Automatic Color Equalization)等。并且提出了关于ACE的快速算法。
第五章是结论和未来的工作,在全面的总结本论文后,提出了将来要作的工作
The problem of the color image rendering was researched in this paper. Based on investigation and analysis of current situation in this field both in our country and abroad, the progressive algorithms and new ones were proposed pointing at kernel techniques such as Tone mapping curves, Tone mapping operators, Retinex and Automatic color equalization. The difficulty of simultaneously raising contrast in both bright and black zone was got through. The processing speed-the choke point of ACE-was successfully disposed by new style convolution. Experiments with effective result support the applications of the color image rendering technique in real world.
In chapter 1, the background, features and processing methods of HDRI (high dynamic range image rendering) and Color Correction are mainly introduced. The relationship of the problems is depicted after the explicit description of the related work of mine.
In chapter 2, the mainstream algorithms in Tone Mapping Technology called TRC (Tone Reproduction Curve) and TRO (Tone Reproduction Operation) based on the feature of human vision system are illustrated. The new fast algorithm that combines the human vision system and histogram is created and introduced in every detail.
In chapter 3, the Retinex technique is thoroughly depicted. After introduction to the conception of Retinex, tow representative models named MMT-99 and MSR (Multi-Scale Retinex) are given. Tow improved algorithms based on the former are announced later. Especially, the new algorithm embedding Retinex into Wavelet shows better effect than the origin.
In chapter 4, the analysis of the generating background and problems confronted to the Color Correction is done. GW (Gray World), ACE (Automatic Color Equalization), the mainly algorithms of the Color Correction , are introduced. The new and fast ACE algorithm is proposed here with explanation of detail steps and test results.
In chapter 5, the conclusions are achieved from this paper and future works are fully discussed later on.
第一章是绪论,主要介绍了动态图像显示和颜色校正两个问题产生的背景,特点和现有处理此问题的方法。并且给出了本文在这个方面的相关工作。并且稍后指出两者的关系。
第二章是调整映射技术,这里我们介绍了人眼视觉系统的特征,描述了基于此的两种主流算法,调整映射曲线和调整映射操作。并将人眼视觉系统和直方图结合起来,得到新的快速调整映射曲线算法。
第三章是Retinex技术,在简单的Retinex介绍之后,给出了两个有代表性的Retinex模型—MMT-99,MSR(Multi-Scale Retinex)。并且基于前者提出了两种改进算法,尤其是后面的改进是将Retinex技术嵌入到小波中,得到良好的效果。
第四章是色彩校正技术,在分析了色彩校正技术产生的背景和所面临的问题后,介绍了一些重要的校正算法,如GW(Gray World),ACE(Automatic Color Equalization)等。并且提出了关于ACE的快速算法。
第五章是结论和未来的工作,在全面的总结本论文后,提出了将来要作的工作
The problem of the color image rendering was researched in this paper. Based on investigation and analysis of current situation in this field both in our country and abroad, the progressive algorithms and new ones were proposed pointing at kernel techniques such as Tone mapping curves, Tone mapping operators, Retinex and Automatic color equalization. The difficulty of simultaneously raising contrast in both bright and black zone was got through. The processing speed-the choke point of ACE-was successfully disposed by new style convolution. Experiments with effective result support the applications of the color image rendering technique in real world.
In chapter 1, the background, features and processing methods of HDRI (high dynamic range image rendering) and Color Correction are mainly introduced. The relationship of the problems is depicted after the explicit description of the related work of mine.
In chapter 2, the mainstream algorithms in Tone Mapping Technology called TRC (Tone Reproduction Curve) and TRO (Tone Reproduction Operation) based on the feature of human vision system are illustrated. The new fast algorithm that combines the human vision system and histogram is created and introduced in every detail.
In chapter 3, the Retinex technique is thoroughly depicted. After introduction to the conception of Retinex, tow representative models named MMT-99 and MSR (Multi-Scale Retinex) are given. Tow improved algorithms based on the former are announced later. Especially, the new algorithm embedding Retinex into Wavelet shows better effect than the origin.
In chapter 4, the analysis of the generating background and problems confronted to the Color Correction is done. GW (Gray World), ACE (Automatic Color Equalization), the mainly algorithms of the Color Correction , are introduced. The new and fast ACE algorithm is proposed here with explanation of detail steps and test results.
In chapter 5, the conclusions are achieved from this paper and future works are fully discussed later on.
引文
[1] J. Tumblin and H. Rushmeier, “Tone reproduction for realistic images”, IEEE Computer Graphics and Applications, vol. 13, pp. 42 – 48, 1993
[2] Available at: http://www.cs.nott.ac.uk/~qiu/hdri/fastmaphdri.html
[3] G. W. Larson, H. Rushmeier and C. Piatko, “A visibility matching tone reproduction operator for high dynamic range scenes”, IEEE Trans on Visualization and Computer Graphics,vol. 3, pp. 291 – 306, 1997
[4] F.Drago, K.Myszkowski, T.Annen and N.Chiba, “Adaptive Logarithmic Mapping For Displaying High Contrast Scenes”, The Journal of Computer Graphics Forum, Vol.22, No, 3, pp. 419-426, 2003
[5] J. Tumblin, J.K. Hodgins, and B.K. Guenter. “Two Methods for Display of High Contrast Images.” ACM Transactions on Graphics, 18(1):56–94, 1999.
[6] Pattanaik, S. N, Ferwerda, J.A., Fairchild, M. D. and Greenberg, D. P. "A Multi-scale Model of Adaptation and Spatial Vision for Realistic Image Display", Proceedings of SIGGRAPH'98, pp 287-298, Orlando, July 1998.
[7] F. Durand and J. Dorsey. “Fast bilateral Filtering for the display of high dynamic range Images.“ In: ACM Transactions on Graphics. Proceedings of SIGGRAPH'2002, pages 257-266, 2002.
[8] E. H. Land, “The retinex theory of color vision”[J], Scientific American. 1977, vol 237(6) 108-128
[9] A. Rizzi, D. Marini, L. Rovati, and F. Docchio, “Unsupervised corrections of unknown chromatic dominants using a brownain-path-based retinex algorithm”[J], Journal of Electronic Imaging, July 2003. vol12(3), pp. 431-440,
[10] Ron Kimmel . “A Variational Framework for Retinex”[J], International Journal of Computer Vision . 2003, 52(1)7-23
[11] J. Frankle and J. McCann, “Method and appartus for lightness imaging"[P], US Patent # 4 384336. May 1983.
[12] Z. Rahman, D. Jobson, and G. Woodell, “Retinex processing for automatic image enhancement"[A], In :IS&T/SPIE Electronic Imaging, The Human Vision and Electronic Imaging 7th Conference, San Jose, 2002,pp. 390-401
[13] L. Meylan and S. Süsstrunk, “Bio-inspired color image enhancement”[A], In: IS&T/SPIE Electronic Imaging. The Human Vision and Electronic Imaging 4thConference, Switzerland, 2004,Vol 5292
[14] F. Gasparini, R. Schettini, Color balancing of digital photos using simple image statistics, Pattern Recognition, Vol. 37 (6) pp 1201-1217, 2004
[15] A. Rizzi, C. Gatta, and D. Marini, “Color correction between gray world and white patch,"[A] In: IS&T/SPIE Electronic Imaging, The Human Vision and Electronic Imaging 7th Conference, San Jose , 2002,pp. 367-375
[16] A. Rizzi, C. Gatta, and D. Marini, “A new algorithm for unsupervised global and local color correction," Pattern Recognition Letters 24(11), pp. 1663-1677, July 2003.
[17] J. Morovic, To Develop a Universal Gamut Mapping Algorithm, Ph. D. Thesis, University of Derby, 1998.
[18] G. Braun, A paradigm for color gamut mapping of pictorial images, Ph. D. Thesis, Rochester Institute of Technology, 1999.
[19] R. Bala, R. deQueiroz, R. Eschbach, W. Wu, \Gamut Mapping to Preserve Spatial Luminance Variations", Journal of Imaging Science and Technology, Vol
[20] J. M. Kasson, “Color image gamut-mapping system with chroma enhancement at human-insensitive spatial frequencies”, US Patent 5450216, 1995.
[21] T.G. Stockham. Image Processing in the Context of a Visual Model, Proceedings of the IEEE, 1972,60:828–842
[22] M. Ashikhmin, “A Tone Mapping Algorithm for High Contrast Images,” Proc. 13th Eurographics Workshop Rendering, pp. 145-155, 2002.
[23]C. Schlick, “Quantization Techniques for the Visualization of High Dynamic Range Pictures,” Photorealistic Rendering Techniques, P. Shirley, G. Sakas, and S. Mu¨ ller, eds., pp. 7-20, Berlin, Heidelberg, New York: Springer-Verlag, 1994.
[24] TUMBLIN, J., HODGINS, J. K., AND GUENTER, B. K. 1999. Two methods for display of high contrast images. ACM Transactions on Graphics 18, 1 (Jan.), 56–94.
[25] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic Tone Reproduction for Digital Images,” ACM Trans. Graphics, vol. 21, no. 3, pp. 267-276, 2002.
[26] E. Reinhard, “Parameter Estimation for Photographic Tone Reproduction,” J. Graphics Tools, vol. 7, no. 1, pp. 45-51, 2003.
[27] R. Fattal, D. Lischinski, and M. Werman, “Gradient Domain High Dynamic Range Compression,” ACM Trans. Graphics, vol. 21, no. 3, pp. 249-256, 2002.
[28] TUMBLIN, J., AND TURK, G.. LCIS: A boundary hierarchy for detail-preserving contrast reduction. In Proc. ACM SIGGRAPH 99, A. Rockwood, Ed., 83–90.
[29]章毓晋 图像处理和图像分析,高等教育出版社,2002.7
[30]Frankle, Jonathan and McCann, John, “Method and Apparatus for Lightness Imaging”, US Patent #4,384,336, May 17, 1983.
[31]. E. Land, “The Retinex,”[J], American Scientist .1964,vol52, pp. 247-264
[32] Funt, B.V., Ciurea, F., and McCann, J.J., “Retinex in Matlab” Proc. IS&T/SID Eighth Color Imaging Conference, 112-121, Scottsdale 2000.
[33] Brown ,”An adaptive strategy for wavelet based image enhancement.” Proceedings of IMVIP 2000 - Irish Machine Vision and Image Processing Conference, 67 - 81, 2000.
[34]Funt, B. and Ciurea, F. “Control Parameters for Retinex”. AIC 2001 Proc 9th Congress of the International Color Association,,June 2001, in press.
[35] G. Healey. Segmenting images using normalized color. IEEE Trans. System, Man and Cybernetics, 22(1):64–73, January 1992.
[36] B.A. Buluswar, S.D.; Draper. Color recognition in outdoor images. In IEEE International Conference on Computer Vision, January 1998.
[37] M. Swain and D. Ballard. Color inidexing. Int. J. Comput. Vision, 7(11), November 1991.
[38]G. Wyszecki and W. S. Stiles, Color science: concepts and methods, quantitative data and formulae, Wiley, New York, 1982.
[39]M.D Fairchild, Color appearance Models Addison Wesley, 1997
[2] Available at: http://www.cs.nott.ac.uk/~qiu/hdri/fastmaphdri.html
[3] G. W. Larson, H. Rushmeier and C. Piatko, “A visibility matching tone reproduction operator for high dynamic range scenes”, IEEE Trans on Visualization and Computer Graphics,vol. 3, pp. 291 – 306, 1997
[4] F.Drago, K.Myszkowski, T.Annen and N.Chiba, “Adaptive Logarithmic Mapping For Displaying High Contrast Scenes”, The Journal of Computer Graphics Forum, Vol.22, No, 3, pp. 419-426, 2003
[5] J. Tumblin, J.K. Hodgins, and B.K. Guenter. “Two Methods for Display of High Contrast Images.” ACM Transactions on Graphics, 18(1):56–94, 1999.
[6] Pattanaik, S. N, Ferwerda, J.A., Fairchild, M. D. and Greenberg, D. P. "A Multi-scale Model of Adaptation and Spatial Vision for Realistic Image Display", Proceedings of SIGGRAPH'98, pp 287-298, Orlando, July 1998.
[7] F. Durand and J. Dorsey. “Fast bilateral Filtering for the display of high dynamic range Images.“ In: ACM Transactions on Graphics. Proceedings of SIGGRAPH'2002, pages 257-266, 2002.
[8] E. H. Land, “The retinex theory of color vision”[J], Scientific American. 1977, vol 237(6) 108-128
[9] A. Rizzi, D. Marini, L. Rovati, and F. Docchio, “Unsupervised corrections of unknown chromatic dominants using a brownain-path-based retinex algorithm”[J], Journal of Electronic Imaging, July 2003. vol12(3), pp. 431-440,
[10] Ron Kimmel . “A Variational Framework for Retinex”[J], International Journal of Computer Vision . 2003, 52(1)7-23
[11] J. Frankle and J. McCann, “Method and appartus for lightness imaging"[P], US Patent # 4 384336. May 1983.
[12] Z. Rahman, D. Jobson, and G. Woodell, “Retinex processing for automatic image enhancement"[A], In :IS&T/SPIE Electronic Imaging, The Human Vision and Electronic Imaging 7th Conference, San Jose, 2002,pp. 390-401
[13] L. Meylan and S. Süsstrunk, “Bio-inspired color image enhancement”[A], In: IS&T/SPIE Electronic Imaging. The Human Vision and Electronic Imaging 4thConference, Switzerland, 2004,Vol 5292
[14] F. Gasparini, R. Schettini, Color balancing of digital photos using simple image statistics, Pattern Recognition, Vol. 37 (6) pp 1201-1217, 2004
[15] A. Rizzi, C. Gatta, and D. Marini, “Color correction between gray world and white patch,"[A] In: IS&T/SPIE Electronic Imaging, The Human Vision and Electronic Imaging 7th Conference, San Jose , 2002,pp. 367-375
[16] A. Rizzi, C. Gatta, and D. Marini, “A new algorithm for unsupervised global and local color correction," Pattern Recognition Letters 24(11), pp. 1663-1677, July 2003.
[17] J. Morovic, To Develop a Universal Gamut Mapping Algorithm, Ph. D. Thesis, University of Derby, 1998.
[18] G. Braun, A paradigm for color gamut mapping of pictorial images, Ph. D. Thesis, Rochester Institute of Technology, 1999.
[19] R. Bala, R. deQueiroz, R. Eschbach, W. Wu, \Gamut Mapping to Preserve Spatial Luminance Variations", Journal of Imaging Science and Technology, Vol
[20] J. M. Kasson, “Color image gamut-mapping system with chroma enhancement at human-insensitive spatial frequencies”, US Patent 5450216, 1995.
[21] T.G. Stockham. Image Processing in the Context of a Visual Model, Proceedings of the IEEE, 1972,60:828–842
[22] M. Ashikhmin, “A Tone Mapping Algorithm for High Contrast Images,” Proc. 13th Eurographics Workshop Rendering, pp. 145-155, 2002.
[23]C. Schlick, “Quantization Techniques for the Visualization of High Dynamic Range Pictures,” Photorealistic Rendering Techniques, P. Shirley, G. Sakas, and S. Mu¨ ller, eds., pp. 7-20, Berlin, Heidelberg, New York: Springer-Verlag, 1994.
[24] TUMBLIN, J., HODGINS, J. K., AND GUENTER, B. K. 1999. Two methods for display of high contrast images. ACM Transactions on Graphics 18, 1 (Jan.), 56–94.
[25] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic Tone Reproduction for Digital Images,” ACM Trans. Graphics, vol. 21, no. 3, pp. 267-276, 2002.
[26] E. Reinhard, “Parameter Estimation for Photographic Tone Reproduction,” J. Graphics Tools, vol. 7, no. 1, pp. 45-51, 2003.
[27] R. Fattal, D. Lischinski, and M. Werman, “Gradient Domain High Dynamic Range Compression,” ACM Trans. Graphics, vol. 21, no. 3, pp. 249-256, 2002.
[28] TUMBLIN, J., AND TURK, G.. LCIS: A boundary hierarchy for detail-preserving contrast reduction. In Proc. ACM SIGGRAPH 99, A. Rockwood, Ed., 83–90.
[29]章毓晋 图像处理和图像分析,高等教育出版社,2002.7
[30]Frankle, Jonathan and McCann, John, “Method and Apparatus for Lightness Imaging”, US Patent #4,384,336, May 17, 1983.
[31]. E. Land, “The Retinex,”[J], American Scientist .1964,vol52, pp. 247-264
[32] Funt, B.V., Ciurea, F., and McCann, J.J., “Retinex in Matlab” Proc. IS&T/SID Eighth Color Imaging Conference, 112-121, Scottsdale 2000.
[33] Brown ,”An adaptive strategy for wavelet based image enhancement.” Proceedings of IMVIP 2000 - Irish Machine Vision and Image Processing Conference, 67 - 81, 2000.
[34]Funt, B. and Ciurea, F. “Control Parameters for Retinex”. AIC 2001 Proc 9th Congress of the International Color Association,,June 2001, in press.
[35] G. Healey. Segmenting images using normalized color. IEEE Trans. System, Man and Cybernetics, 22(1):64–73, January 1992.
[36] B.A. Buluswar, S.D.; Draper. Color recognition in outdoor images. In IEEE International Conference on Computer Vision, January 1998.
[37] M. Swain and D. Ballard. Color inidexing. Int. J. Comput. Vision, 7(11), November 1991.
[38]G. Wyszecki and W. S. Stiles, Color science: concepts and methods, quantitative data and formulae, Wiley, New York, 1982.
[39]M.D Fairchild, Color appearance Models Addison Wesley, 1997