Difference between revisions of "Publications/esteban.19.caip"
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abstract = <nowiki>{</nowiki>The knowledge of the noise level within an image is a |
abstract = <nowiki>{</nowiki>The knowledge of the noise level within an image is a |
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valuableinformation for many image processing applications. |
valuableinformation for many image processing applications. |
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Latest revision as of 13:30, 13 July 2023
- Authors
- Baptiste Esteban, Guillaume Tochon, Thierry Géraud
- Where
- Proceedings of the 18th International Conference on Computer Analysis of Images and Patterns (CAIP)
- Place
- Salerno, Italy
- Type
- inproceedings
- Publisher
- Springer
- Keywords
- Image
- Date
- 2019-06-07
Abstract
The knowledge of the noise level within an image is a valuableinformation for many image processing applications. Estimating the noise level function (NLF) requires the identification of homogeneous regions, upon which the noise parameters are computed. Sutour et al. have proposed a method to estimate this NLF based on the search for homogeneous regions of square shape. We generalize this method to the search for homogeneous regions with arbitrary shape thanks to the tree of shapes representation of the image under study, thus allowing a more robust and precise estimation of the noise level function.
Documents
Bibtex (lrde.bib)
@InProceedings{ esteban.19.caip,
author = {Baptiste Esteban and Guillaume Tochon and Thierry
G\'eraud},
title = {Estimating the noise level function with the tree of
shapes and non-parametric statistics},
booktitle = {Proceedings of the 18th International Conference on
Computer Analysis of Images and Patterns (CAIP)},
year = 2019,
pages = {377--388},
series = {Lecture Notes in Computer Science Series},
volume = {11679},
publisher = {Springer},
address = {Salerno, Italy},
month = sep,
doi = {10.1007/978-3-030-29891-3_33},
abstract = {The knowledge of the noise level within an image is a
valuableinformation for many image processing applications.
Estimating the noise level function (NLF) requires the
identification of homogeneous regions, upon which the noise
parameters are computed. Sutour et al. have proposed a
method to estimate this NLF based on the search for
homogeneous regions of square shape. We generalize this
method to the search for homogeneous regions with arbitrary
shape thanks to the tree of shapes representation of the
image under study, thus allowing a more robust and precise
estimation of the noise level function.}
}