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Conference Paper

Demosaicing by Smoothing along 1D Features

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons43981

Ajdin,  Boris
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44654

Hullin,  Matthias B.
Computer Graphics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44454

Fuchs,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44911

Lensch,  Hendrik P. A.
Computer Graphics, MPI for Informatics, Max Planck Society;

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Citation

Ajdin, B., Hullin, M. B., Fuchs, C., Seidel, H.-P., & Lensch, H. P. A. (2008). Demosaicing by Smoothing along 1D Features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008) (pp. 2423-2430). Los Alamitos, CA: IEEE Computer Society.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1B65-F
Abstract
Most digital cameras capture color pictures in the form of an image mosaic, recording only one color channel at each pixel position. Therefore, an interpolation algorithm needs to be applied to reconstruct the missing color information. In this paper we present a novel Bayer pattern demosaicing approach, employing stochastic global optimization performed on a pixel neighborhood. We are minimizing a newly developed cost function that increases smoothness along one-dimensional image features. While previous algorithms have been developed focusing on LDR images only, our optimization scheme and the underlying cost function are designed to handle both LDR and HDR images, creating less demosaicing artifacts, compared to previous approaches.