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International Joint Conference on Computer Vision, Imaging and Computer Graphics

VISIGRAPP 2018: Computer Vision, Imaging and Computer Graphics Theory and Applications pp 281–297Cite as

Random Forests Based Image Colorization

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Part of the Communications in Computer and Information Science book series (CCIS,volume 997)

Abstract

The task of image colorization, i.e. assigninging color values to grayscale images, is usually addressed by either exploiting explicit user input or very large training data sets. In contrast, the proposed method is fully automatic and uses several orders of magnitude less training images. To this aim, a Random Forest is tailored to the task of regressing plausible color value given a patch of the grayscale image. In order to improve the colorization performance, the Random Forests also includes a simple position prior. The proposed approach leads to satisfying results over various colorization tasks and compares favorably with the state of the art based on convolutional networks.

Keywords

  • Image colorization
  • Random forests
  • Regression

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  • DOI: 10.1007/978-3-030-26756-8_14
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Fig. 1.

Image taken from our previous work [6] with identical general workflow.

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Notes

  1. 1.

    The error maps are contrast enhanced for better visibility in print.

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Author information

Authors and Affiliations

  1. Computer Vision and Remote Sensing, Technische Universität Berlin, Berlin, Germany

    Helge Mohn, Mark Gaebelein, Ronny Hänsch & Olaf Hellwich

Authors
  1. Helge Mohn
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  2. Mark Gaebelein
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  3. Ronny Hänsch
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  4. Olaf Hellwich
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Corresponding author

Correspondence to Ronny Hänsch .

Editor information

Editors and Affiliations

  1. University of Strasbourg, Strasbourg, France

    Prof. Dominique Bechmann

  2. University of Genoa, Genoa, Italy

    Manuela Chessa

  3. University of Lisbon, Lisbon, Portugal

    Ana Paula Cláudio

  4. Research Innovation Center, Apple Inc., San Jose, CA, USA

    Francisco Imai

  5. Linnaeus University, Växjö, Kronobergs Län, Sweden

    Prof. Dr. Andreas Kerren

  6. LISA - ISTIA, University of Angers, Angers, France

    Paul Richard

  7. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  8. Jean Monnet University, Saint-Etienne, France

    Prof. Alain Tremeau

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Mohn, H., Gaebelein, M., Hänsch, R., Hellwich, O. (2019). Random Forests Based Image Colorization. In: , et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2018. Communications in Computer and Information Science, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-26756-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-26756-8_14

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