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Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising
Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of i...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305082/ https://www.ncbi.nlm.nih.gov/pubmed/37420705 http://dx.doi.org/10.3390/s23125538 |
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author | Jóźwik-Wabik, Piotr Bernacki, Krzysztof Popowicz, Adam |
author_facet | Jóźwik-Wabik, Piotr Bernacki, Krzysztof Popowicz, Adam |
author_sort | Jóźwik-Wabik, Piotr |
collection | PubMed |
description | Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of imaging is often affected by noise, which significantly degrades the quality of the results. In order to reduce it, numerous deterministic algorithms are used, with Non-Local-Means and Block-Matching-3D being the most widespread and treated as the reference point of the current state-of-the-art. Our article focuses on the utilization of machine learning (ML) for the denoising of monochromatic images in multiple data availability scenarios, including those with no access to noise-free data. For this purpose, a simple autoencoder architecture was chosen and checked for various training approaches on two large and widely used image datasets: MNIST and CIFAR-10. The results show that the method of training as well as architecture and the similarity of images within the image dataset significantly affect the ML-based denoising. However, even without access to any clear data, the performance of such algorithms is frequently well above the current state-of-the-art; therefore, they should be considered for monochromatic image denoising. |
format | Online Article Text |
id | pubmed-10305082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103050822023-06-29 Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising Jóźwik-Wabik, Piotr Bernacki, Krzysztof Popowicz, Adam Sensors (Basel) Article Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of imaging is often affected by noise, which significantly degrades the quality of the results. In order to reduce it, numerous deterministic algorithms are used, with Non-Local-Means and Block-Matching-3D being the most widespread and treated as the reference point of the current state-of-the-art. Our article focuses on the utilization of machine learning (ML) for the denoising of monochromatic images in multiple data availability scenarios, including those with no access to noise-free data. For this purpose, a simple autoencoder architecture was chosen and checked for various training approaches on two large and widely used image datasets: MNIST and CIFAR-10. The results show that the method of training as well as architecture and the similarity of images within the image dataset significantly affect the ML-based denoising. However, even without access to any clear data, the performance of such algorithms is frequently well above the current state-of-the-art; therefore, they should be considered for monochromatic image denoising. MDPI 2023-06-13 /pmc/articles/PMC10305082/ /pubmed/37420705 http://dx.doi.org/10.3390/s23125538 Text en © 2023 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 Jóźwik-Wabik, Piotr Bernacki, Krzysztof Popowicz, Adam Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising |
title | Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising |
title_full | Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising |
title_fullStr | Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising |
title_full_unstemmed | Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising |
title_short | Comparison of Training Strategies for Autoencoder-Based Monochromatic Image Denoising |
title_sort | comparison of training strategies for autoencoder-based monochromatic image denoising |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305082/ https://www.ncbi.nlm.nih.gov/pubmed/37420705 http://dx.doi.org/10.3390/s23125538 |
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