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Deep Learning Network for Speckle De-Noising in Severe Conditions
Digital holography is well adapted to measure any modifications related to any objects. The method refers to digital holographic interferometry where the phase change between two states of the object is of interest. However, the phase images are corrupted by the speckle decorrelation noise. In this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225311/ https://www.ncbi.nlm.nih.gov/pubmed/35735964 http://dx.doi.org/10.3390/jimaging8060165 |
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author | Tahon, Marie Montrésor, Silvio Picart, Pascal |
author_facet | Tahon, Marie Montrésor, Silvio Picart, Pascal |
author_sort | Tahon, Marie |
collection | PubMed |
description | Digital holography is well adapted to measure any modifications related to any objects. The method refers to digital holographic interferometry where the phase change between two states of the object is of interest. However, the phase images are corrupted by the speckle decorrelation noise. In this paper, we address the question of de-noising in holographic interferometry when phase data are polluted with speckle noise. We present a new database of phase fringe images for the evaluation of de-noising algorithms in digital holography. In this database, the simulated phase maps present characteristics such as the size of the speckle grains and the noise level of the fringes, which can be controlled by the generation process. Deep neural network architectures are trained with sets of phase maps having differentiated parameters according to the features. The performances of the new models are evaluated with a set of test fringe patterns whose characteristics are representative of severe conditions in terms of input SNR and speckle grain size. For this, four metrics are considered, which are the PSNR, the phase error, the perceived quality index and the peak-to-valley ratio. Results demonstrate that the models trained with phase maps with a diversity of noise characteristics lead to improving their efficiency, their robustness and their generality on phase maps with severe noise. |
format | Online Article Text |
id | pubmed-9225311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92253112022-06-24 Deep Learning Network for Speckle De-Noising in Severe Conditions Tahon, Marie Montrésor, Silvio Picart, Pascal J Imaging Article Digital holography is well adapted to measure any modifications related to any objects. The method refers to digital holographic interferometry where the phase change between two states of the object is of interest. However, the phase images are corrupted by the speckle decorrelation noise. In this paper, we address the question of de-noising in holographic interferometry when phase data are polluted with speckle noise. We present a new database of phase fringe images for the evaluation of de-noising algorithms in digital holography. In this database, the simulated phase maps present characteristics such as the size of the speckle grains and the noise level of the fringes, which can be controlled by the generation process. Deep neural network architectures are trained with sets of phase maps having differentiated parameters according to the features. The performances of the new models are evaluated with a set of test fringe patterns whose characteristics are representative of severe conditions in terms of input SNR and speckle grain size. For this, four metrics are considered, which are the PSNR, the phase error, the perceived quality index and the peak-to-valley ratio. Results demonstrate that the models trained with phase maps with a diversity of noise characteristics lead to improving their efficiency, their robustness and their generality on phase maps with severe noise. MDPI 2022-06-09 /pmc/articles/PMC9225311/ /pubmed/35735964 http://dx.doi.org/10.3390/jimaging8060165 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tahon, Marie Montrésor, Silvio Picart, Pascal Deep Learning Network for Speckle De-Noising in Severe Conditions |
title | Deep Learning Network for Speckle De-Noising in Severe Conditions |
title_full | Deep Learning Network for Speckle De-Noising in Severe Conditions |
title_fullStr | Deep Learning Network for Speckle De-Noising in Severe Conditions |
title_full_unstemmed | Deep Learning Network for Speckle De-Noising in Severe Conditions |
title_short | Deep Learning Network for Speckle De-Noising in Severe Conditions |
title_sort | deep learning network for speckle de-noising in severe conditions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225311/ https://www.ncbi.nlm.nih.gov/pubmed/35735964 http://dx.doi.org/10.3390/jimaging8060165 |
work_keys_str_mv | AT tahonmarie deeplearningnetworkforspeckledenoisinginsevereconditions AT montresorsilvio deeplearningnetworkforspeckledenoisinginsevereconditions AT picartpascal deeplearningnetworkforspeckledenoisinginsevereconditions |