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Democratic Tone Mapping Using Optimal K-means Clustering

Oskarsson, Magnus LU (2015) 19th Scandinavian Conference on Image Analysis (SCIA 2015) In Lecture Notes in Computer Science (Image Analysis, 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings)) 9127. p.354-365
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Tone mapping, image processing, clustering, high dynamic range
in
Lecture Notes in Computer Science (Image Analysis, 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings))
editor
Paulsen, Rasmus; Pedersen, Kim; and
volume
9127
pages
12 pages
publisher
Springer
conference name
19th Scandinavian Conference on Image Analysis (SCIA 2015)
external identifiers
  • scopus:84947937864
ISSN
0302-9743
1611-3349
ISBN
978-3-319-19665-7
978-3-319-19664-0
DOI
10.1007/978-3-319-19665-7_29
language
English
LU publication?
yes
id
f74e52e9-1a7e-45dd-9e7a-72080b58ed65 (old id 8052122)
alternative location
http://link.springer.com/chapter/10.1007/978-3-319-19665-7_29
date added to LUP
2016-01-20 17:49:10
date last changed
2017-06-11 03:02:56
@inproceedings{f74e52e9-1a7e-45dd-9e7a-72080b58ed65,
  abstract     = {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.},
  author       = {Oskarsson, Magnus},
  booktitle    = {Lecture Notes in Computer Science (Image Analysis, 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings))},
  editor       = {Paulsen, Rasmus and Pedersen, Kim},
  isbn         = {978-3-319-19665-7},
  issn         = {0302-9743},
  keyword      = {Tone mapping,image processing,clustering,high dynamic range},
  language     = {eng},
  pages        = {354--365},
  publisher    = {Springer},
  title        = {Democratic Tone Mapping Using Optimal K-means Clustering},
  url          = {http://dx.doi.org/10.1007/978-3-319-19665-7_29},
  volume       = {9127},
  year         = {2015},
}