Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection
The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes b...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Shengjie [verfasserIn] Cui, Hao [verfasserIn] Li, Junwei [verfasserIn] Yao, Mulin [verfasserIn] Wang, Shengqian [verfasserIn] Wei, Kai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Infrared physics & technology - Amsterdam [u.a.] : Elsevier Science, 1994, 133 |
|---|---|
| Übergeordnetes Werk: |
volume:133 |
| DOI / URN: |
10.1016/j.infrared.2023.104799 |
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| Katalog-ID: |
ELV062486748 |
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| 245 | 1 | 0 | |a Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection |
| 264 | 1 | |c 2023 | |
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| 337 | |a Computermedien |b c |2 rdamedia | ||
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| 520 | |a The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. | ||
| 650 | 4 | |a Nonuniformity correction | |
| 650 | 4 | |a Infrared image | |
| 650 | 4 | |a Feature matching | |
| 650 | 4 | |a Image registration | |
| 650 | 4 | |a Kalman filter | |
| 650 | 4 | |a Learning rate adjustment | |
| 700 | 1 | |a Cui, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Junwei |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Mulin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shengqian |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Kai |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Infrared physics & technology |d Amsterdam [u.a.] : Elsevier Science, 1994 |g 133 |h Online-Ressource |w (DE-627)320592146 |w (DE-600)2019084-0 |w (DE-576)259271705 |7 nnns |
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| 936 | b | k | |a 50.37 |j Technische Optik |q VZ |
| 936 | b | k | |a 33.38 |j Quantenoptik |j nichtlineare Optik |q VZ |
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10.1016/j.infrared.2023.104799 doi (DE-627)ELV062486748 (ELSEVIER)S1350-4495(23)00257-8 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Liu, Shengjie verfasserin aut Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. Nonuniformity correction Infrared image Feature matching Image registration Kalman filter Learning rate adjustment Cui, Hao verfasserin aut Li, Junwei verfasserin aut Yao, Mulin verfasserin aut Wang, Shengqian verfasserin aut Wei, Kai verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 133 |
| spelling |
10.1016/j.infrared.2023.104799 doi (DE-627)ELV062486748 (ELSEVIER)S1350-4495(23)00257-8 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Liu, Shengjie verfasserin aut Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. Nonuniformity correction Infrared image Feature matching Image registration Kalman filter Learning rate adjustment Cui, Hao verfasserin aut Li, Junwei verfasserin aut Yao, Mulin verfasserin aut Wang, Shengqian verfasserin aut Wei, Kai verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 133 |
| allfields_unstemmed |
10.1016/j.infrared.2023.104799 doi (DE-627)ELV062486748 (ELSEVIER)S1350-4495(23)00257-8 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Liu, Shengjie verfasserin aut Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. Nonuniformity correction Infrared image Feature matching Image registration Kalman filter Learning rate adjustment Cui, Hao verfasserin aut Li, Junwei verfasserin aut Yao, Mulin verfasserin aut Wang, Shengqian verfasserin aut Wei, Kai verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 133 |
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10.1016/j.infrared.2023.104799 doi (DE-627)ELV062486748 (ELSEVIER)S1350-4495(23)00257-8 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Liu, Shengjie verfasserin aut Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. Nonuniformity correction Infrared image Feature matching Image registration Kalman filter Learning rate adjustment Cui, Hao verfasserin aut Li, Junwei verfasserin aut Yao, Mulin verfasserin aut Wang, Shengqian verfasserin aut Wei, Kai verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 133 |
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10.1016/j.infrared.2023.104799 doi (DE-627)ELV062486748 (ELSEVIER)S1350-4495(23)00257-8 DE-627 ger DE-627 rda eng 530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Liu, Shengjie verfasserin aut Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. Nonuniformity correction Infrared image Feature matching Image registration Kalman filter Learning rate adjustment Cui, Hao verfasserin aut Li, Junwei verfasserin aut Yao, Mulin verfasserin aut Wang, Shengqian verfasserin aut Wei, Kai verfasserin aut Enthalten in Infrared physics & technology Amsterdam [u.a.] : Elsevier Science, 1994 133 Online-Ressource (DE-627)320592146 (DE-600)2019084-0 (DE-576)259271705 nnns volume:133 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.37 Technische Optik VZ 33.38 Quantenoptik nichtlineare Optik VZ 33.07 Spektroskopie VZ AR 133 |
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Liu, Shengjie @@aut@@ Cui, Hao @@aut@@ Li, Junwei @@aut@@ Yao, Mulin @@aut@@ Wang, Shengqian @@aut@@ Wei, Kai @@aut@@ |
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Liu, Shengjie |
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Liu, Shengjie ddc 530 bkl 50.37 bkl 33.38 bkl 33.07 misc Nonuniformity correction misc Infrared image misc Feature matching misc Image registration misc Kalman filter misc Learning rate adjustment Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection |
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530 VZ 50.37 bkl 33.38 bkl 33.07 bkl Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection Nonuniformity correction Infrared image Feature matching Image registration Kalman filter Learning rate adjustment |
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Liu, Shengjie Cui, Hao Li, Junwei Yao, Mulin Wang, Shengqian Wei, Kai |
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low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection |
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Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection |
| abstract |
The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. |
| abstractGer |
The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. |
| abstract_unstemmed |
The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method. |
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Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection |
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| score |
7.3974895 |