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A 2D image 3D reconstruction function adaptive denoising algorithm
To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into re...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557518/ https://www.ncbi.nlm.nih.gov/pubmed/37810338 http://dx.doi.org/10.7717/peerj-cs.1604 |
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author | Wang, Feng Ni, Weichuan Liu, Shaojiang Xu, Zhiming Qiu, Zemin Wan, Zhiping |
author_facet | Wang, Feng Ni, Weichuan Liu, Shaojiang Xu, Zhiming Qiu, Zemin Wan, Zhiping |
author_sort | Wang, Feng |
collection | PubMed |
description | To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image’s detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target’s feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction. |
format | Online Article Text |
id | pubmed-10557518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105575182023-10-07 A 2D image 3D reconstruction function adaptive denoising algorithm Wang, Feng Ni, Weichuan Liu, Shaojiang Xu, Zhiming Qiu, Zemin Wan, Zhiping PeerJ Comput Sci Algorithms and Analysis of Algorithms To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image’s detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target’s feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction. PeerJ Inc. 2023-10-03 /pmc/articles/PMC10557518/ /pubmed/37810338 http://dx.doi.org/10.7717/peerj-cs.1604 Text en ©2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Wang, Feng Ni, Weichuan Liu, Shaojiang Xu, Zhiming Qiu, Zemin Wan, Zhiping A 2D image 3D reconstruction function adaptive denoising algorithm |
title | A 2D image 3D reconstruction function adaptive denoising algorithm |
title_full | A 2D image 3D reconstruction function adaptive denoising algorithm |
title_fullStr | A 2D image 3D reconstruction function adaptive denoising algorithm |
title_full_unstemmed | A 2D image 3D reconstruction function adaptive denoising algorithm |
title_short | A 2D image 3D reconstruction function adaptive denoising algorithm |
title_sort | 2d image 3d reconstruction function adaptive denoising algorithm |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557518/ https://www.ncbi.nlm.nih.gov/pubmed/37810338 http://dx.doi.org/10.7717/peerj-cs.1604 |
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