The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Visando as desvantagens do algoritmo de correção de não uniformidade (NUC) de redes neurais tradicionais, como convergência lenta, baixa precisão de correção e dificuldade para atender aos requisitos de aplicação de engenharia em tempo real do sistema de imagem infravermelha, um algoritmo NUC aprimorado para matrizes de plano focal infravermelho (IRFPA) baseado em rede neural é proposto. O algoritmo é baseado na resposta linear do detector e, para realizar uma convergência rápida e sincronizada dos parâmetros de correção, cada dado de imagem original é normalizado para um valor próximo a um. Resultados experimentais mostram que o método tem velocidade de convergência mais rápida e melhor efeito de visão do que os algoritmos tradicionais, e é melhor aplicado em projetos práticos.
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Shao-sheng DAI, Tian-qi ZHANG, "An Improved Non-uniformity Correction Algorithm for IRFPA Based on Neural Network" in IEICE TRANSACTIONS on Electronics,
vol. E92-C, no. 5, pp. 736-739, May 2009, doi: 10.1587/transele.E92.C.736.
Abstract: Aiming at traditional neural networks non-uniformity correction (NUC) algorithm's disadvantages such as slow convergence, low correction precision and difficulty to meet the real-time engineering application requirements of infrared imaging system, an improved NUC algorithm for infrared focal plane arrays (IRFPA) based on neural network is proposed. The algorithm is based on linear response of detector, and in order to realize fast and synchronization convergence of correction parameters the each original image data is normalized to a value close to one. Experimental results show the method has the faster convergence speed and better vision effect than the traditional algorithms, and it is better applied in practical projects.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.E92.C.736/_p
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@ARTICLE{e92-c_5_736,
author={Shao-sheng DAI, Tian-qi ZHANG, },
journal={IEICE TRANSACTIONS on Electronics},
title={An Improved Non-uniformity Correction Algorithm for IRFPA Based on Neural Network},
year={2009},
volume={E92-C},
number={5},
pages={736-739},
abstract={Aiming at traditional neural networks non-uniformity correction (NUC) algorithm's disadvantages such as slow convergence, low correction precision and difficulty to meet the real-time engineering application requirements of infrared imaging system, an improved NUC algorithm for infrared focal plane arrays (IRFPA) based on neural network is proposed. The algorithm is based on linear response of detector, and in order to realize fast and synchronization convergence of correction parameters the each original image data is normalized to a value close to one. Experimental results show the method has the faster convergence speed and better vision effect than the traditional algorithms, and it is better applied in practical projects.},
keywords={},
doi={10.1587/transele.E92.C.736},
ISSN={1745-1353},
month={May},}
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TY - JOUR
TI - An Improved Non-uniformity Correction Algorithm for IRFPA Based on Neural Network
T2 - IEICE TRANSACTIONS on Electronics
SP - 736
EP - 739
AU - Shao-sheng DAI
AU - Tian-qi ZHANG
PY - 2009
DO - 10.1587/transele.E92.C.736
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E92-C
IS - 5
JA - IEICE TRANSACTIONS on Electronics
Y1 - May 2009
AB - Aiming at traditional neural networks non-uniformity correction (NUC) algorithm's disadvantages such as slow convergence, low correction precision and difficulty to meet the real-time engineering application requirements of infrared imaging system, an improved NUC algorithm for infrared focal plane arrays (IRFPA) based on neural network is proposed. The algorithm is based on linear response of detector, and in order to realize fast and synchronization convergence of correction parameters the each original image data is normalized to a value close to one. Experimental results show the method has the faster convergence speed and better vision effect than the traditional algorithms, and it is better applied in practical projects.
ER -