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Practical Implementation of LMMSE Demosaicing Using Luminance and Chrominance Spaces (2006)
| Citations: | 7 - 1 self |
BibTeX
@MISC{Lavarène06practicalimplementation,
author = {Brice Chaix de Lavarène and David Alleysson and Jeanny Hérault},
title = {Practical Implementation of LMMSE Demosaicing Using Luminance and Chrominance Spaces},
year = {2006}
}
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Abstract
Most digital color cameras sample only one color at each spatial location, using a single sensor coupled with a color filter array (CFA). An interpolation step called demosaicing (or demosaicking) is required for rendering a color image from the acquired CFA image. Already proposed Linear Minimum Mean Square Error (LMMSE) demosaicing provides a good tradeoff between quality and computational cost for embedded systems. In this paper we propose a modification of the stacked notation of superpixels, which allows an effective computing of the LMMSE solution from an image database. Moreover, this formalism is used to decompose the CFA sampling into a sum of a luminance estimator and a chrominance projector. This decomposition allows interpreting estimated filters in term of their spatial and chromatic properties and results in a solution with lower computational complexity than other LMMSE approaches for the same quality.
Keyphrases
chrominance space practical implementation lmmse demosaicing using luminance color filter array single sensor chromatic property stacked notation color image image database spatial location chrominance projector interpolation step luminance estimator lmmse solution effective computing good tradeoff computational cost cfa image linear minimum mean square error digital color camera computational complexity lmmse approach embedded system
