Practical implementation of LMMSE demosaicing using luminance and chrominance spaces

  • Authors:

  • Brice Chaix de Lavarène;David Alleysson;Jeanny Hérault

  • Affiliations:

  • Images and Signals Laboratory (LIS), Joseph Fourier University, Grenoble, France;Psychology and Neurocognition Laboratory (LPNC), Pierre Mendès France University, Grenoble, France;Images and Signals Laboratory (LIS), Joseph Fourier University, Grenoble, France

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2007

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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.