Estimation of the noise level function for color images using mathematical morphology and non-parametric statistics

From LRDE

Revision as of 09:48, 20 June 2022 by Bot (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Abstract

Noise level information is crucial for many image processing tasks, such as image denoising. To estimate itit is necessary to find homegeneous areas within the image which contain only noise. Rank-based methods have proven to be efficient to achieve such a task. In the past, we proposed a method to estimate the noise level function (NLF) of grayscale images using the tree of shapes (ToS). This method, relying on the connected components extracted from the ToS computed on the noisy image, had the advantage of being adapted to the image content, which is not the case when using square blocks, but is still restricted to grayscale images. In this paper, we extend our ToS-based method to color images. Unlike grayscale images, the pixel values in multivariate images do not have a natural order relationship, which is a well-known issue when working with mathematical morphology and rank statistics. We propose to use the multivariate ToS to retrieve homogeneous regions. We derive an order relationship for the multivariate pixel values thanks to a complete lattice learning strategy and use it to compute the rank statistics. The obtained multivariate NLF is composed of one NLF per channel. The performance of the proposed method is compared with the one obtained using square blocks, and validates the soundness of the multivariate ToS structure for this task.

Documents

Bibtex (lrde.bib)

@InProceedings{	  esteban.22.icpr,
  author	= {Baptiste Esteban and Guillaume Tochon and Edwin Carlinet
		  and Didier Verna},
  title		= {Estimation of the noise level function for color images
		  using mathematical morphology and non-parametric
		  statistics},
  booktitle	= {Proceedings of the 26th International Conference on
		  Pattern Recognition},
  year		= 2022,
  address	= {Montr\'eal, Qu\'ebec},
  month		= aug,
  abstract	= {Noise level information is crucial for many image
		  processing tasks, such as image denoising. To estimate it,
		  it is necessary to find homegeneous areas within the image
		  which contain only noise. Rank-based methods have proven to
		  be efficient to achieve such a task. In the past, we
		  proposed a method to estimate the noise level function
		  (NLF) of grayscale images using the tree of shapes (ToS).
		  This method, relying on the connected components extracted
		  from the ToS computed on the noisy image, had the advantage
		  of being adapted to the image content, which is not the
		  case when using square blocks, but is still restricted to
		  grayscale images. In this paper, we extend our ToS-based
		  method to color images. Unlike grayscale images, the pixel
		  values in multivariate images do not have a natural order
		  relationship, which is a well-known issue when working with
		  mathematical morphology and rank statistics. We propose to
		  use the multivariate ToS to retrieve homogeneous regions.
		  We derive an order relationship for the multivariate pixel
		  values thanks to a complete lattice learning strategy and
		  use it to compute the rank statistics. The obtained
		  multivariate NLF is composed of one NLF per channel. The
		  performance of the proposed method is compared with the one
		  obtained using square blocks, and validates the soundness
		  of the multivariate ToS structure for this task. },
  note		= {accepted}
}