An Improved Non-uniformity Correction Algorithm for IRFPA Based on Neural Network
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Electronics Vol.E92-C No.5 pp.736-739
- Publication Date
- 2009/05/01
- Publicized
- Online ISSN
- 1745-1353
- DOI
- 10.1587/transele.E92.C.736
- Type of Manuscript
- LETTER
- Category
- Optoelectronics
Authors
Keyword
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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},}
Copy
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 -
English
