Democratic Tone Mapping Using Optimal K-means Clustering
Oskarsson, Magnus LU
(2015)
19th Scandinavian Conference on Image Analysis (SCIA 2015)
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:
https://lup.lub.lu.se/record/8052122
- author
- Oskarsson, Magnus
LU
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Tone mapping, image processing, clustering, high dynamic range
- host publication
- 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
- volume
- 9127
- pages
- 12 pages
- publisher
- Springer
- conference name
- 19th Scandinavian Conference on Image Analysis (SCIA 2015)
- conference location
- Copenhagen, Denmark
- conference dates
- 2015-06-15 - 2015-06-17
- 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
- 2016-04-01 09:58:00
- date last changed
- 2024-02-04 16:33:17
@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}},
keywords = {{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}},
doi = {{10.1007/978-3-319-19665-7_29}},
volume = {{9127}},
year = {{2015}},
}