Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology
Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial f...
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| Autor*in: |
Petković, Dalibor [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Pleiades Publishing, Ltd. 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Optics and spectroscopy - Pleiades Publishing, 1959, 117(2014), 1 vom: Juli, Seite 121-131 |
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| Übergeordnetes Werk: |
volume:117 ; year:2014 ; number:1 ; month:07 ; pages:121-131 |
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| DOI / URN: |
10.1134/S0030400X14070042 |
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| 520 | |a Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. | ||
| 650 | 4 | |a Membership Function | |
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| 700 | 1 | |a Pavlović, Nenad T. |4 aut | |
| 700 | 1 | |a Anuar, Nor Badrul |4 aut | |
| 700 | 1 | |a Kiah, Miss Laiha Mat |4 aut | |
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10.1134/S0030400X14070042 doi (DE-627)OLC2047088933 (DE-He213)S0030400X14070042-p DE-627 ger DE-627 rakwb eng 530 VZ 11 ssgn Petković, Dalibor verfasserin aut Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2014 Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. Membership Function Spatial Frequency Fuzzy Inference System Modulation Transfer Function Adaptive NEURO Fuzzy Inference System Shamshirband, Shahaboddin aut Pavlović, Nenad T. aut Anuar, Nor Badrul aut Kiah, Miss Laiha Mat aut Enthalten in Optics and spectroscopy Pleiades Publishing, 1959 117(2014), 1 vom: Juli, Seite 121-131 (DE-627)129496499 (DE-600)207391-2 (DE-576)014895048 0030-400X nnns volume:117 year:2014 number:1 month:07 pages:121-131 https://doi.org/10.1134/S0030400X14070042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_60 GBV_ILN_70 AR 117 2014 1 07 121-131 |
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10.1134/S0030400X14070042 doi (DE-627)OLC2047088933 (DE-He213)S0030400X14070042-p DE-627 ger DE-627 rakwb eng 530 VZ 11 ssgn Petković, Dalibor verfasserin aut Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2014 Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. Membership Function Spatial Frequency Fuzzy Inference System Modulation Transfer Function Adaptive NEURO Fuzzy Inference System Shamshirband, Shahaboddin aut Pavlović, Nenad T. aut Anuar, Nor Badrul aut Kiah, Miss Laiha Mat aut Enthalten in Optics and spectroscopy Pleiades Publishing, 1959 117(2014), 1 vom: Juli, Seite 121-131 (DE-627)129496499 (DE-600)207391-2 (DE-576)014895048 0030-400X nnns volume:117 year:2014 number:1 month:07 pages:121-131 https://doi.org/10.1134/S0030400X14070042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_60 GBV_ILN_70 AR 117 2014 1 07 121-131 |
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10.1134/S0030400X14070042 doi (DE-627)OLC2047088933 (DE-He213)S0030400X14070042-p DE-627 ger DE-627 rakwb eng 530 VZ 11 ssgn Petković, Dalibor verfasserin aut Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2014 Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. Membership Function Spatial Frequency Fuzzy Inference System Modulation Transfer Function Adaptive NEURO Fuzzy Inference System Shamshirband, Shahaboddin aut Pavlović, Nenad T. aut Anuar, Nor Badrul aut Kiah, Miss Laiha Mat aut Enthalten in Optics and spectroscopy Pleiades Publishing, 1959 117(2014), 1 vom: Juli, Seite 121-131 (DE-627)129496499 (DE-600)207391-2 (DE-576)014895048 0030-400X nnns volume:117 year:2014 number:1 month:07 pages:121-131 https://doi.org/10.1134/S0030400X14070042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_60 GBV_ILN_70 AR 117 2014 1 07 121-131 |
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10.1134/S0030400X14070042 doi (DE-627)OLC2047088933 (DE-He213)S0030400X14070042-p DE-627 ger DE-627 rakwb eng 530 VZ 11 ssgn Petković, Dalibor verfasserin aut Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2014 Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. Membership Function Spatial Frequency Fuzzy Inference System Modulation Transfer Function Adaptive NEURO Fuzzy Inference System Shamshirband, Shahaboddin aut Pavlović, Nenad T. aut Anuar, Nor Badrul aut Kiah, Miss Laiha Mat aut Enthalten in Optics and spectroscopy Pleiades Publishing, 1959 117(2014), 1 vom: Juli, Seite 121-131 (DE-627)129496499 (DE-600)207391-2 (DE-576)014895048 0030-400X nnns volume:117 year:2014 number:1 month:07 pages:121-131 https://doi.org/10.1134/S0030400X14070042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_60 GBV_ILN_70 AR 117 2014 1 07 121-131 |
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10.1134/S0030400X14070042 doi (DE-627)OLC2047088933 (DE-He213)S0030400X14070042-p DE-627 ger DE-627 rakwb eng 530 VZ 11 ssgn Petković, Dalibor verfasserin aut Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2014 Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. Membership Function Spatial Frequency Fuzzy Inference System Modulation Transfer Function Adaptive NEURO Fuzzy Inference System Shamshirband, Shahaboddin aut Pavlović, Nenad T. aut Anuar, Nor Badrul aut Kiah, Miss Laiha Mat aut Enthalten in Optics and spectroscopy Pleiades Publishing, 1959 117(2014), 1 vom: Juli, Seite 121-131 (DE-627)129496499 (DE-600)207391-2 (DE-576)014895048 0030-400X nnns volume:117 year:2014 number:1 month:07 pages:121-131 https://doi.org/10.1134/S0030400X14070042 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY GBV_ILN_60 GBV_ILN_70 AR 117 2014 1 07 121-131 |
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Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology |
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Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. © Pleiades Publishing, Ltd. 2014 |
| abstractGer |
Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. © Pleiades Publishing, Ltd. 2014 |
| abstract_unstemmed |
Abstract The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. © Pleiades Publishing, Ltd. 2014 |
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Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology |
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Shamshirband, Shahaboddin Pavlović, Nenad T. Anuar, Nor Badrul Kiah, Miss Laiha Mat |
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Shamshirband, Shahaboddin Pavlović, Nenad T. Anuar, Nor Badrul Kiah, Miss Laiha Mat |
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10.1134/S0030400X14070042 |
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