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Adaptive Monte Carlo Retinex Method for Illumination and Reflectance Separation and Color Image Enhancement

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3 Author(s)
Alexander Wong ; Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada ; David A. Clausi ; Paul Fieguth

A novel stochastic Retinex method based on adaptive Monte Carlo estimation is presented for the purpose of illumination and reflectance separation and color image enhancement. A spatially-adaptive sampling scheme is employed to generate a set of random samples from the image field. A Monte Carlo estimate of the illumination is computed based on the Pearson Type VII error statistics of the drawn samples. The proposed method takes advantage of both local and global contrast information to provide better separation of reflectance and illumination by reducing the effects of strong shadows and other sharp illumination changes on the estimation process, improving the preservation of the original photographic tone, and avoiding the amplification of noise in dark regions. Experimental results using monochromatic face images under different illumination conditions and low-contrast chromatic images show the effectiveness of the proposed method for illumination and reflectance separation and color image enhancement when compared to existing Retinex and color enhancement techniques.

Published in:

Computer and Robot Vision, 2009. CRV '09. Canadian Conference on

Date of Conference:

25-27 May 2009