Paper Info
Title
Democratic Tone Mapping Using Optimal K-means Clustering.
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.
Year
Venue
Field
2015
SCIA
Computer vision,k-means clustering,Dynamic programming,Pattern recognition,Computer science,Image processing,Tone mapping,Artificial intelligence,Cluster analysis,Luminance,High dynamic range,High-dynamic-range imaging
DocType
Citations 
PageRank 
Conference
3
0.36
References 
Authors
13
1
Name
Order
Citations
PageRank
Magnus Oskarsson119622.85