Estimating the noise level function with the tree of shapes and non-parametric statistics

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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,
  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.}
}