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Democratic Tone Mapping Using Optimal K-means Clustering

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Author

Editor

  • Rasmus Paulsen
  • Kim Pedersen

Summary, in English

The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on $K$-means clustering. Using dynamic programming we are able to, not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in O(N^2K) for an image with N luminance levels and K output levels. We show that our algorithm gives comparable result to state-of-the-art tone mapping algorithms, but with the additional large benefit of a total lack of parameters. We test our algorithm on a number of standard high dynamic range images, and give qualitative comparisons to a number of state-of-the-art tone mapping algorithms.

Publishing year

2015

Language

English

Pages

354-365

Publication/Series

Lecture Notes in Computer Science (Image Analysis, 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings))

Volume

9127

Document type

Conference paper

Publisher

Springer

Topic

  • Discrete Mathematics
  • Computer Science

Keywords

  • Tone mapping
  • image processing
  • clustering
  • high dynamic range

Conference name

19th Scandinavian Conference on Image Analysis (SCIA 2015)

Conference date

2015-06-15 - 2015-06-17

Conference place

Copenhagen, Denmark

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 1611-3349
  • ISSN: 0302-9743
  • ISBN: 978-3-319-19665-7
  • ISBN: 978-3-319-19664-0