A histogram specification technique for dark image enhancement using a local transformation method
Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local tr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hussain, Khalid [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2018 |
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| Übergeordnetes Werk: |
Enthalten in: IPSJ Transactions on Computer Vision and Applications - Tōkyō : IPSJ, 2009, 10(2018), 1 vom: 12. Feb. |
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| Übergeordnetes Werk: |
volume:10 ; year:2018 ; number:1 ; day:12 ; month:02 |
| Links: |
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| DOI / URN: |
10.1186/s41074-018-0040-0 |
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SPR038190206 |
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| 520 | |a Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. | ||
| 650 | 4 | |a Dark image enhancement |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Histogram equalization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Histogram specification |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Rahman, Shanto |4 aut | |
| 700 | 1 | |a Rahman, Md. Mostafijur |4 aut | |
| 700 | 1 | |a Khaled, Shah Mostafa |4 aut | |
| 700 | 1 | |a Abdullah-Al Wadud, M. |4 aut | |
| 700 | 1 | |a Hossain Khan, Muhammad Asif |4 aut | |
| 700 | 1 | |a Shoyaib, Mohammad |4 aut | |
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10.1186/s41074-018-0040-0 doi (DE-627)SPR038190206 (SPR)s41074-018-0040-0-e DE-627 ger DE-627 rakwb eng Hussain, Khalid verfasserin aut A histogram specification technique for dark image enhancement using a local transformation method 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. Dark image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Histogram specification (dpeaa)DE-He213 Rahman, Shanto aut Rahman, Md. Mostafijur aut Khaled, Shah Mostafa aut Abdullah-Al Wadud, M. aut Hossain Khan, Muhammad Asif aut Shoyaib, Mohammad aut Enthalten in IPSJ Transactions on Computer Vision and Applications Tōkyō : IPSJ, 2009 10(2018), 1 vom: 12. Feb. (DE-627)78570406X (DE-600)2769752-6 1882-6695 nnns volume:10 year:2018 number:1 day:12 month:02 https://dx.doi.org/10.1186/s41074-018-0040-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 12 02 |
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10.1186/s41074-018-0040-0 doi (DE-627)SPR038190206 (SPR)s41074-018-0040-0-e DE-627 ger DE-627 rakwb eng Hussain, Khalid verfasserin aut A histogram specification technique for dark image enhancement using a local transformation method 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. Dark image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Histogram specification (dpeaa)DE-He213 Rahman, Shanto aut Rahman, Md. Mostafijur aut Khaled, Shah Mostafa aut Abdullah-Al Wadud, M. aut Hossain Khan, Muhammad Asif aut Shoyaib, Mohammad aut Enthalten in IPSJ Transactions on Computer Vision and Applications Tōkyō : IPSJ, 2009 10(2018), 1 vom: 12. Feb. (DE-627)78570406X (DE-600)2769752-6 1882-6695 nnns volume:10 year:2018 number:1 day:12 month:02 https://dx.doi.org/10.1186/s41074-018-0040-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 12 02 |
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10.1186/s41074-018-0040-0 doi (DE-627)SPR038190206 (SPR)s41074-018-0040-0-e DE-627 ger DE-627 rakwb eng Hussain, Khalid verfasserin aut A histogram specification technique for dark image enhancement using a local transformation method 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. Dark image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Histogram specification (dpeaa)DE-He213 Rahman, Shanto aut Rahman, Md. Mostafijur aut Khaled, Shah Mostafa aut Abdullah-Al Wadud, M. aut Hossain Khan, Muhammad Asif aut Shoyaib, Mohammad aut Enthalten in IPSJ Transactions on Computer Vision and Applications Tōkyō : IPSJ, 2009 10(2018), 1 vom: 12. Feb. (DE-627)78570406X (DE-600)2769752-6 1882-6695 nnns volume:10 year:2018 number:1 day:12 month:02 https://dx.doi.org/10.1186/s41074-018-0040-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 12 02 |
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10.1186/s41074-018-0040-0 doi (DE-627)SPR038190206 (SPR)s41074-018-0040-0-e DE-627 ger DE-627 rakwb eng Hussain, Khalid verfasserin aut A histogram specification technique for dark image enhancement using a local transformation method 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. Dark image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Histogram specification (dpeaa)DE-He213 Rahman, Shanto aut Rahman, Md. Mostafijur aut Khaled, Shah Mostafa aut Abdullah-Al Wadud, M. aut Hossain Khan, Muhammad Asif aut Shoyaib, Mohammad aut Enthalten in IPSJ Transactions on Computer Vision and Applications Tōkyō : IPSJ, 2009 10(2018), 1 vom: 12. Feb. (DE-627)78570406X (DE-600)2769752-6 1882-6695 nnns volume:10 year:2018 number:1 day:12 month:02 https://dx.doi.org/10.1186/s41074-018-0040-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 12 02 |
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10.1186/s41074-018-0040-0 doi (DE-627)SPR038190206 (SPR)s41074-018-0040-0-e DE-627 ger DE-627 rakwb eng Hussain, Khalid verfasserin aut A histogram specification technique for dark image enhancement using a local transformation method 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. Dark image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Histogram specification (dpeaa)DE-He213 Rahman, Shanto aut Rahman, Md. Mostafijur aut Khaled, Shah Mostafa aut Abdullah-Al Wadud, M. aut Hossain Khan, Muhammad Asif aut Shoyaib, Mohammad aut Enthalten in IPSJ Transactions on Computer Vision and Applications Tōkyō : IPSJ, 2009 10(2018), 1 vom: 12. Feb. (DE-627)78570406X (DE-600)2769752-6 1882-6695 nnns volume:10 year:2018 number:1 day:12 month:02 https://dx.doi.org/10.1186/s41074-018-0040-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 12 02 |
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histogram specification technique for dark image enhancement using a local transformation method |
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A histogram specification technique for dark image enhancement using a local transformation method |
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Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. © The Author(s) 2018 |
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Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. © The Author(s) 2018 |
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Abstract Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques. © The Author(s) 2018 |
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