Unsupervised corrections of unknown chromatic dominants using a Brownian-path-based Retinex algorithm

An experimental analysis of chromatic equalization based on a new implementation of the Retinex algorithm is pre- sented. The experiments are carried out on a colored Mondrian patchwork illuminated with different commercial light sources and on synthetic images generated with a photometric ray tracer using dif- ferent illuminants. Regarding the Mondrian patchwork, the spectral characteristics of the bulbs and the reflected light from each patch are measured using a commercial spectrometer. From the mea- sured data, synthetic images of the patchwork with different illumi- nants are created and processed by the Retinex algorithm. The chromatic correction capabilities of the Retinex implementation have been measured and compared with unfiltered values and with the results of another Retinex implementation and classic color equal- ization algorithms. Results show that Retinex performs an unsuper- vised color correction without requiring any information about the spectral composition of the illuminant. © 2003 SPIE and IS&T.

[1]  Rodney M. Goodman,et al.  A real-time neural system for color constancy , 1991, IEEE Trans. Neural Networks.

[2]  E. Land The retinex theory of color vision. , 1977, Scientific American.

[3]  John J. McCann Color constancy: small overall and large local changes , 1992, Electronic Imaging.

[4]  W D Wright,et al.  Color Science, Concepts and Methods. Quantitative Data and Formulas , 1967 .

[5]  Brian V. Funt,et al.  Investigations into Multi-Scale Retinex , 1998 .

[6]  J. McCann Rules for colour constancy , 1992, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[7]  Alessandro Rizzi,et al.  Color appearance approach to image database visual retrieval , 1999, Electronic Imaging.

[8]  John J. McCann,et al.  Lessons Learned from Mondrians Applied to Real Images and Color Gamuts , 1999, CIC.

[9]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .

[10]  E. Marg A VISION OF THE BRAIN , 1994 .

[11]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[12]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[13]  S. McKee,et al.  Quantitative studies in retinex theory a comparison between theoretical predictions and observer responses to the “color mondrian” experiments , 1976, Vision Research.

[14]  Mark S. Drew,et al.  Color constancy from mutual reflection , 1991, International Journal of Computer Vision.

[15]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[16]  Brian V. Funt,et al.  Tuning Retinex parameters , 2004, J. Electronic Imaging.

[17]  Paul C. Bressloff,et al.  Visual cortex and the Retinex algorithm , 2002, IS&T/SPIE Electronic Imaging.

[18]  Alessandro Rizzi,et al.  A computational approach to color adaptation effects , 2000, Image Vis. Comput..

[19]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .

[20]  Raimondo Schettini,et al.  Retinex preprocessing of uncalibrated images for color-based image retrieval , 2003, J. Electronic Imaging.

[21]  Graham D. Finlayson,et al.  Color by Correlation , 1997, CIC.

[22]  Alessandro Rizzi,et al.  Color constancy measurements for synthetic image generation , 1999, J. Electronic Imaging.