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Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream
Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevit...
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/PMC10422208/ https://www.ncbi.nlm.nih.gov/pubmed/37571439 http://dx.doi.org/10.3390/s23156655 |
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author | Tang, Sichao Lv, Hengyi Zhao, Yuchen Feng, Yang Liu, Hailong Bi, Guoling |
author_facet | Tang, Sichao Lv, Hengyi Zhao, Yuchen Feng, Yang Liu, Hailong Bi, Guoling |
author_sort | Tang, Sichao |
collection | PubMed |
description | Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevitably present in the camera’s output results. This noise can degrade the camera’s perception of events and the performance of algorithms for processing event streams. Moreover, since the output of event cameras is in the form of address-event representation, efficient denoising methods for traditional frame images are no longer applicable in this case. Most existing denoising methods for event cameras target background activity noise and sometimes remove real events as noise. Furthermore, these methods are ineffective in handling noise generated by high-frequency flickering light sources and changes in diffused light reflection. To address these issues, we propose an event stream denoising method based on salient region recognition in this paper. This method can effectively remove conventional background activity noise as well as irregular noise caused by diffuse reflection and flickering light source changes without significantly losing real events. Additionally, we introduce an evaluation metric that can be used to assess the noise removal efficacy and the preservation of real events for various denoising methods. |
format | Online Article Text |
id | pubmed-10422208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104222082023-08-13 Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream Tang, Sichao Lv, Hengyi Zhao, Yuchen Feng, Yang Liu, Hailong Bi, Guoling Sensors (Basel) Article Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevitably present in the camera’s output results. This noise can degrade the camera’s perception of events and the performance of algorithms for processing event streams. Moreover, since the output of event cameras is in the form of address-event representation, efficient denoising methods for traditional frame images are no longer applicable in this case. Most existing denoising methods for event cameras target background activity noise and sometimes remove real events as noise. Furthermore, these methods are ineffective in handling noise generated by high-frequency flickering light sources and changes in diffused light reflection. To address these issues, we propose an event stream denoising method based on salient region recognition in this paper. This method can effectively remove conventional background activity noise as well as irregular noise caused by diffuse reflection and flickering light source changes without significantly losing real events. Additionally, we introduce an evaluation metric that can be used to assess the noise removal efficacy and the preservation of real events for various denoising methods. MDPI 2023-07-25 /pmc/articles/PMC10422208/ /pubmed/37571439 http://dx.doi.org/10.3390/s23156655 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 Tang, Sichao Lv, Hengyi Zhao, Yuchen Feng, Yang Liu, Hailong Bi, Guoling Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream |
title | Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream |
title_full | Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream |
title_fullStr | Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream |
title_full_unstemmed | Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream |
title_short | Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream |
title_sort | denoising method based on salient region recognition for the spatiotemporal event stream |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422208/ https://www.ncbi.nlm.nih.gov/pubmed/37571439 http://dx.doi.org/10.3390/s23156655 |
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