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Issue No.03 - March (2009 vol.31)
pp: 458-474
Rodrigo Palma-Amestoy , Universidad de Chile , Santiago
Edoardo Provenzi , Università di Milano, Crema
Marcelo Bertalmío , Universitat Pompeu Fabra, Barcelona
Vincent Caselles , Universitat Pompeu Fabra, Barcelona
ABSTRACT
Basic phenomenology of human color vision has been widely taken as an inspiration to devise explicit color correction algorithms. The behavior of these models in terms of significative image features (such as, e.g., contrast and dispersion) can be difficult to characterize. To cope with this, we propose to use a variational formulation of color contrast enhancement that is inspired by the basic phenomenology of color perception. In particular, we devise a set of basic requirements to be fulfilled by an energy to be considered as 'perceptually inspired', showing that there is an explicit class of functionals satisfying all of them. We single out three explicit functionals that we consider of basic interest, showing similarities and differences with existing models. The minima of such functionals is computed using a gradient descent approach. We also present a general methodology to reduce the computational cost of the algorithms under analysis from O(N2) to O(N logN), being N the number of pixels of the input image.
INDEX TERMS
Constrained optimization, Gradient methods, Partial Differential Equations, Iterative solution techniques, Enhancement, Filtering, Color
CITATION
Rodrigo Palma-Amestoy, Edoardo Provenzi, Marcelo Bertalmío, Vincent Caselles, "A Perceptually Inspired Variational Framework for Color Enhancement", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 3, pp. 458-474, March 2009, doi:10.1109/TPAMI.2008.86
REFERENCES
[1] R. Gregory, Eye and Brain. Princeton Univ. Press, 1997.
[2] D. Hubel, Eye, Brain, and Vision. Scientific Am. Library, 1995.
[3] S. Palmer, Vision Science: Photons to Phenomenology. MIT Press, 1999.
[4] W. Pratt, Digital Image Processing. John Wiley & Sons, 2007.
[5] B. Wandell, Foundation of Vision. Sinauer Associates, 1995.
[6] G. Wyszecky and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas. John Wiley & Sons, 2000.
[7] B. Wandell, Vision of the Brain. Blackwell Publishing, 1993.
[8] E. Land and J. McCann, “Lightness and Retinex Theory,” J. Optical Soc. of Am., vol. 61, no. 1, pp. 1-11, Jan. 1971.
[9] E. Land, “The Retinex Theory of Color Vision,” Scientific Am., vol. 237, pp. 108-128, 1977.
[10] E. Land, “Recent Advances in Retinex Theory and Some Implications for Cortical Computations: Color Vision and the Natural Image,” Proc. Nat'l Academy Sciences USA, vol. 80, pp.5163-5169, 1983.
[11] S. Zeki and L. Marini, “Three Cortical Stages of Colour Processing in the Human Brain,” Brain, vol. 121, pp. 1669-1685, 1998.
[12] J.J. McCann et al., “Special Session on Retinex at 40,” J. Electronic Imaging, vol. 13, no. 1, pp. 6-145, Jan. 2004.
[13] A. Rizzi, C. Gatta, and D. Marini, “A New Algorithm for Unsupervised Global and Local Color Correction,” Pattern Recognition Letters, vol. 24, pp. 1663-1677, 2003.
[14] G. West, “Color Perception and the Limits of Color Constancy,” J.Math. Biology, vol. 8, pp. 47-53, 1979.
[15] B. Funt, K. Barnard, and L. Martin, “Is Colour Constancy Good Enough?” Proc. Fifth European Conf. Computer Vision, pp. 445-459, 1998.
[16] M. Ebner, Color Constancy. John Wiley & Sons, 2007.
[17] A. Hurlbert, “Formal Connections between Lightness Algorithms,” J. Optical Soc. Am. A, vol. 3, pp. 1684-1693, 1986.
[18] R. Gonzales and R. Woods, Digital Image Processing. Prentice Hall, 2002.
[19] O. Creutzfeld, B. Lange-Malecki, and K. Wortmann, “Darkness Induction, Retinex and Cooperative Mechanisms in Vision,” Experimental Brain Research, vol. 67, pp. 270-283, 1987.
[20] O. Creutzfeld, B. Lange-Malecki, and E. Dreyer, “Darkness Induction, Retinex and Cooperative Mechanisms in Vision,” J.Optical Soc. Am. A, vol. 7, pp. 1644-1653, 1990.
[21] L. Hurvich and D. Jameson, “Theory of Brightness and Color Contrast in Human Vision,” Vision Research, vol. 4, pp. 135-154, 1990.
[22] Q. Zaidi, Color and Brightness Induction: From Mach Bands to Three-Dimensional Configurations. Cambridge Univ. Press, 1999.
[23] R. Shapley and C. Enroth-Cugell, Visual Adaptation and Retinal Gain Controls, vol. 3, pp. 263-346, 1984.
[24] G. Buchsbaum, “A Spatial Processor Model for Object Colour Perception,” J. Franklin Inst., vol. 310, pp. 337-350, 1980.
[25] S. Usui and S. Nakauchi, “A Neurocomputational Model for Color Constancy,” John Dalton's Colour Vision Legacy—Selected Proc. Int'l Conf., C. Dickinson, I. Murray, and D. Carden, eds., pp. 475-482, Taylor and Francis, 1997.
[26] A. Michelson, Studies in Optics. Chicago Univ. Press, 1927.
[27] L. Ambrosio, N. Gigli, and G. Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures, Birkhauser, 2005.
[28] G. Sapiro and V. Caselles, “Histogram Modification via Differential Equations,” J. Differential Equations, vol. 135, pp. 238-266, 1997.
[29] M. Bertalmío, V. Caselles, E. Provenzi, and A. Rizzi, “Perceptual Color Correction through Variational Techniques,” IEEE Trans. Image Processing, vol. 16, pp. 1058-1072, 2007.
[30] E. Provenzi, L. De Carli, A. Rizzi, and D. Marini, “Mathematical Definition and Analysis of the Retinex Algorithm,” J. Optical Soc. of Am. A, vol. 22, no. 12, pp. 2613-2621, Dec. 2005.
[31] E. Provenzi, M. Fierro, A. Rizzi, L. De Carli, D. Gadia, and D. Marini, “Random Spray Retinex: A New Retinex Implementation to Investigate the Local Properties of the Model,” IEEE Trans. Image Processing, vol. 16, pp. 162-171, Jan. 2007.
[32] E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “Spatially Variant White Patch and Gray World Method for Color Image Enhancement Driven by Local Contrast,” IEEE Trans. Pattern Analysis and Machine Intelligence, to appear
[33] J.L. Mannos and D.J. Sakrison, “The Effects of a Visual Fidelity Criterion on the Encoding of Images,” IEEE Trans. Information Theory, vol. 20, no. 4, pp. 525-536, 1974.
[34] P. Soille, Morphological Image Analysis, Principles and Applications. Springer, 1999.
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