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Characterizing the Structure Tensor Using Gamma Distributions

Oskarsson, Magnus LU (2017) 2016 23rd International Conference on Pattern Recognition (ICPR 2016) In Pattern Recognition (ICPR), 2016 23rd International Conference on p.763-768
Abstract (Swedish)
The structure tensor is a powerful tool describing the local intensity structure of an image or image sequence. In this paper we give a model for the noise distribution of the components of the tensor. In order to do so we have also investigated some properties of the gamma distribution. We show that, given an input image corrupted with Gaussian noise, the noise in the structure tensor can be modeled well by gamma distributions. We apply our model to automatic contrast enhancement of images taken under poor illumination. We show how our noise model can be used for automatic parameter selection in the filtering process, giving powerful results without the need for cumbersome parameter tuning.
Abstract
The structure tensor is a powerful tool describing the local intensity structure of an image or image sequence. In this paper we give a model for the noise distribution of the components of the tensor. In order to do so we have also investigated some properties of the gamma distribution. We show that, given an input image corrupted with Gaussian noise, the noise in the structure tensor can be modeled well by gamma distributions. We apply our model to automatic contrast enhancement of images taken under poor illumination. We show how our noise model can be used for automatic parameter selection in the filtering process, giving powerful results without the need for cumbersome parameter tuning.
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
Enhancement, restoration and filtering, Signal, image and video processing, Computational photography
in
Pattern Recognition (ICPR), 2016 23rd International Conference on
pages
6 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2016 23rd International Conference on Pattern Recognition (ICPR 2016)
external identifiers
  • scopus:85019092215
ISBN
978-1-5090-4847-2
DOI
10.1109/ICPR.2016.7899727
language
English
LU publication?
yes
id
3a7667c8-6add-41f6-a31f-f4601968c163
date added to LUP
2016-12-12 15:47:09
date last changed
2018-03-02 09:55:28
@inproceedings{3a7667c8-6add-41f6-a31f-f4601968c163,
  abstract     = {The structure tensor is a powerful tool describing the local intensity structure of an image or image sequence. In this paper we give a model for the noise distribution of the components of the tensor. In order to do so we have also investigated some properties of the gamma distribution. We show that, given an input image corrupted with Gaussian noise, the noise in the structure tensor can be modeled well by gamma distributions. We apply our model to automatic contrast enhancement of images taken under poor illumination. We show how our noise model can be used for automatic parameter selection in the filtering process, giving powerful results without the need for cumbersome parameter tuning. },
  author       = {Oskarsson, Magnus},
  booktitle    = { Pattern Recognition (ICPR), 2016 23rd International Conference on},
  isbn         = {978-1-5090-4847-2 },
  keyword      = {Enhancement,restoration and filtering,Signal, image and video processing,Computational photography},
  language     = {eng},
  month        = {04},
  pages        = {763--768},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Characterizing the Structure Tensor Using Gamma Distributions},
  url          = {http://dx.doi.org/10.1109/ICPR.2016.7899727},
  year         = {2017},
}