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Denoising based on non-local means for ultrasound images with simultaneous multiple noise distributions

Denis Salvadeo Isabelle Bloch 1, 2 Florence Tupin 1, 2 Nelson Mascarenhas Alexandre Levada Charles-Alban Deledalle Sonia Dahdouh 1, 2
1 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
LTCI - Laboratoire Traitement et Communication de l'Information
2 TSI - Département Traitement du Signal et des Images
Abstract : In this paper, an extension of the framework proposed by Deledalle et al.[1] for Non Local Means (NLM) method is proposed. This extension is a general adaptive method to denoise images containing multiple noises. It takes into account a segmentation stage that indicates the noise type of a given pixel in order to select the similarity measure and suitable parameters to perform the denoising task, considering a certain patch on the image. For instance, it has been experimentally observed that fetal 3D ultrasound images are corrupted by different types of noise, depending on the tissue. Finally, the proposed method is applied to denoise these images, showing very good results.
Keywords : ultrasound image image denoising non local means multiple noises ultrasound segmentation
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Conference papers
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https://hal.telecom-paris.fr/hal-02286884
Contributor : Telecomparis Hal <>
Submitted on : Friday, September 13, 2019 - 4:18:53 PM
Last modification on : Wednesday, September 30, 2020 - 8:54:14 AM

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  • HAL Id : hal-02286884, version 1

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LTCI | CNRS | PARISTECH | INSTITUT-TELECOM | IDS | IMAGES

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Denis Salvadeo, Isabelle Bloch, Florence Tupin, Nelson Mascarenhas, Alexandre Levada, et al.. Denoising based on non-local means for ultrasound images with simultaneous multiple noise distributions. IEEE International Conference on Image Processing, Oct 2014, Paris, France. pp.2699-2703. ⟨hal-02286884⟩

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