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Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stat...
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/PMC10490096/ https://www.ncbi.nlm.nih.gov/pubmed/37687800 http://dx.doi.org/10.3390/s23177344 |
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author | Cherniak, Gil Nemirovsky, Jonathan Nemirovsky, Amikam Nemirovsky, Yael |
author_facet | Cherniak, Gil Nemirovsky, Jonathan Nemirovsky, Amikam Nemirovsky, Yael |
author_sort | Cherniak, Gil |
collection | PubMed |
description | A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stationary conditions occur in gated imaging applications. This new stochastic model, which is based on fundamental physical considerations, enlightens us with new insights into gated CMOS imaging, regardless of the sensor. The Signal-to-Noise Ratio (SNR) is simulated, allowing optimized performance. The conversion gain should be determined under stationary conditions. |
format | Online Article Text |
id | pubmed-10490096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104900962023-09-09 Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions Cherniak, Gil Nemirovsky, Jonathan Nemirovsky, Amikam Nemirovsky, Yael Sensors (Basel) Communication A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stationary conditions occur in gated imaging applications. This new stochastic model, which is based on fundamental physical considerations, enlightens us with new insights into gated CMOS imaging, regardless of the sensor. The Signal-to-Noise Ratio (SNR) is simulated, allowing optimized performance. The conversion gain should be determined under stationary conditions. MDPI 2023-08-23 /pmc/articles/PMC10490096/ /pubmed/37687800 http://dx.doi.org/10.3390/s23177344 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 | Communication Cherniak, Gil Nemirovsky, Jonathan Nemirovsky, Amikam Nemirovsky, Yael Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions |
title | Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions |
title_full | Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions |
title_fullStr | Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions |
title_full_unstemmed | Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions |
title_short | Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions |
title_sort | modeling signal-to-noise ratio of cmos image sensors with a stochastic approach under non-stationary conditions |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490096/ https://www.ncbi.nlm.nih.gov/pubmed/37687800 http://dx.doi.org/10.3390/s23177344 |
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