Video and image bayesian demosaicing with a two color image prior

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Video and image bayesian demosaicing with a two color image prior (2006)

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by Eric P. Bennett , Matthew Uyttendaele , C. Lawrencezitnick , Richard Szeliski , Sing Bing Kang

Venue:	In ECCV
Citations:	13 - 4 self

BibTeX

@INPROCEEDINGS{Bennett06videoand,
    author = {Eric P. Bennett and Matthew Uyttendaele and C. Lawrencezitnick and Richard Szeliski and Sing Bing Kang},
    title = {Video and image bayesian demosaicing with a two color image prior},
    booktitle = {In ECCV},
    year = {2006},
    pages = {508--521}
}

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Abstract

Abstract. The demosaicing process converts single-CCD color representations of one color channel per pixel into full per-pixel RGB. We introduce a Bayesian technique for demosaicing Bayer color filter array patterns that is based on a statistically-obtained two color per-pixel image prior. By modeling all local color behavior as a linear combination of two fully specified RGB triples, we avoid color fringing artifacts while preserving sharp edges. Our grid-less, floating-point pixel location architecture can process both single images and multiple images from video within the same framework, with multiple images providing denser color samples and therefore better color reproduction with reduced aliasing. An initial clustering is performed to determine the underlying local two color model surrounding each pixel. Using a product of Gaussians statistical model, the underlying linear blending ratio of the two representative colors at each pixel is estimated, while simultaneously providing noise reduction. Finally, we show that by sampling the image model at a finer resolution than the source images during reconstruction, our continuous demosaicing technique can super-resolve in a single step. 1

Keyphrases

color image multiple image color channel bayer color filter array pattern single image color reproduction process convert single-ccd color representation local color behavior representative color color per-pixel image noise reduction initial clustering floating-point pixel location architecture color model single step linear combination color fringing artifact full per-pixel rgb bayesian technique rgb triple image model gaussians statistical model continuous demosaicing technique sharp edge source image denser color sample

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