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Proceedings Paper

Bayesian edge-preserving color image reconstruction from color filter array data
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Paper Abstract

Digital still cameras typically use a single optical sensor overlaid with RGB color filters to acquire a scene. Only one of the three primary colors is observed at each pixel and the full color image must be reconstructed (demosaicked) from available data. We consider the problem of demosaicking for images sampled in the commonly used Bayer pattern. The full color image is obtained from the sampled data as a MAP estimate. To exploit the greater sampling rate in the green channel in defining the presence of edges in the blue and red channels, a Gaussian MRF model that considers the presence of edges in all three color channels is used to define a prior. Pixel values and edge estimates are computed iteratively using an algorithm based on Besag's iterated conditional modes (ICM) algorithm. The reconstruction algorithm iterates alternately to perform edge detection and spatial smoothing. The proposed algorithm is applied to a variety of test images and its performance is quantified by using the CIELAB delta E measure.

Paper Details

Date Published: 11 March 2005
PDF: 10 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.597627
Show Author Affiliations
Manu Parmar, Auburn Univ. (United States)
Stanley J. Reeves, Auburn Univ. (United States)
Thomas S. Denney, Auburn Univ. (United States)


Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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