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Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints †
In this paper, we propose a general method to detect outliers from contaminated estimates of various image estimation applications. The method does not require any prior knowledge about the purpose, theory or hardware of the application but simply relies on the law of edge consistency between source...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038852/ https://www.ncbi.nlm.nih.gov/pubmed/33917476 http://dx.doi.org/10.3390/s21072563 |
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author | Zhu, Mingzhu Hu, Yaoqing Yu, Junzhi He, Bingwei Liu, Jiantao |
author_facet | Zhu, Mingzhu Hu, Yaoqing Yu, Junzhi He, Bingwei Liu, Jiantao |
author_sort | Zhu, Mingzhu |
collection | PubMed |
description | In this paper, we propose a general method to detect outliers from contaminated estimates of various image estimation applications. The method does not require any prior knowledge about the purpose, theory or hardware of the application but simply relies on the law of edge consistency between sources and estimates. The method is termed as ALRe (anchored linear residual) because it is based on the residual of weighted local linear regression with an equality constraint exerted on the measured pixel. Given a pair of source and contaminated estimate, ALRe offers per-pixel outlier likelihoods, which can be used to compose the data weights of post-refinement algorithms, improving the quality of refined estimate. ALRe has the features of asymmetry, no false positive and linear complexity. Its effectiveness is verified on four applications, four post-refinement algorithms and three datasets. It demonstrates that, with the help of ALRe, refined estimates are better in the aspects of both quality and edge consistency. The results are even comparable to model-based and hardware-based methods. Accuracy comparison on synthetic images shows that ALRe could detect outliers reliably. It is as effective as the mainstream weighted median filter at spike detection and is significantly better at bad region detection. |
format | Online Article Text |
id | pubmed-8038852 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80388522021-04-12 Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † Zhu, Mingzhu Hu, Yaoqing Yu, Junzhi He, Bingwei Liu, Jiantao Sensors (Basel) Article In this paper, we propose a general method to detect outliers from contaminated estimates of various image estimation applications. The method does not require any prior knowledge about the purpose, theory or hardware of the application but simply relies on the law of edge consistency between sources and estimates. The method is termed as ALRe (anchored linear residual) because it is based on the residual of weighted local linear regression with an equality constraint exerted on the measured pixel. Given a pair of source and contaminated estimate, ALRe offers per-pixel outlier likelihoods, which can be used to compose the data weights of post-refinement algorithms, improving the quality of refined estimate. ALRe has the features of asymmetry, no false positive and linear complexity. Its effectiveness is verified on four applications, four post-refinement algorithms and three datasets. It demonstrates that, with the help of ALRe, refined estimates are better in the aspects of both quality and edge consistency. The results are even comparable to model-based and hardware-based methods. Accuracy comparison on synthetic images shows that ALRe could detect outliers reliably. It is as effective as the mainstream weighted median filter at spike detection and is significantly better at bad region detection. MDPI 2021-04-06 /pmc/articles/PMC8038852/ /pubmed/33917476 http://dx.doi.org/10.3390/s21072563 Text en © 2021 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 Zhu, Mingzhu Hu, Yaoqing Yu, Junzhi He, Bingwei Liu, Jiantao Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † |
title | Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † |
title_full | Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † |
title_fullStr | Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † |
title_full_unstemmed | Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † |
title_short | Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints † |
title_sort | find outliers of image edge consistency by weighted local linear regression with equality constraints † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038852/ https://www.ncbi.nlm.nih.gov/pubmed/33917476 http://dx.doi.org/10.3390/s21072563 |
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