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Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces

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Abstract

Motivated by the setting of reproducing kernel Hilbert space (RKHS) and its extensions considered in machine learning, we propose an RKHS framework for image and video colorization. We review and study RKHS especially in vectorial cases and provide various extensions for colorization problems. Theory as well as a practical algorithm is proposed with a number of numerical experiments.

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Author information

Authors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Minh Ha Quang

  2. School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA

    Sung Ha Kang

  3. Department of Mathematics, Yale University, New Haven, CT, USA

    Triet M. Le

Authors
  1. Minh Ha Quang
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  2. Sung Ha Kang
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  3. Triet M. Le
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Corresponding author

Correspondence to Minh Ha Quang.

Additional information

The collaboration began at Hausdorff Research Institute for Mathematics, Bonn, Germany, via support of the Junior Program in Analysis. This work is partially supported by DFG:GZ WI 1515/2-1, NSF:DMS-0908517, NSF: DMS-0809270, and ONR N000140910108.

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Ha Quang, M., Kang, S.H. & Le, T.M. Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces. J Math Imaging Vis 37, 49–65 (2010). https://doi.org/10.1007/s10851-010-0192-8

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