Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies

Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation st...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Teoh, Sin Hoong [verfasserIn] Ibrahim, Haidi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Digital image processing Impulse noise Salt-and-pepper noise Switching median filter Impulse noise detection Impulse noise cancellation

Anmerkung:	© Springer-Verlag London 2013

Übergeordnetes Werk:	Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 8(2013), 2 vom: 24. Aug., Seite 223-242
Übergeordnetes Werk:	volume:8 ; year:2013 ; number:2 ; day:24 ; month:08 ; pages:223-242

Links:	Volltext

DOI / URN:	10.1007/s11760-013-0538-y

Katalog-ID:	SPR022267840

Internformat


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245	1	0	\|a Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
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520			\|a Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques.
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700	1		\|a Ibrahim, Haidi \|4 aut
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allfields	10.1007/s11760-013-0538-y doi (DE-627)SPR022267840 (SPR)s11760-013-0538-y-e DE-627 ger DE-627 rakwb eng Teoh, Sin Hoong verfasserin aut Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. Digital image processing (dpeaa)DE-He213 Impulse noise (dpeaa)DE-He213 Salt-and-pepper noise (dpeaa)DE-He213 Switching median filter (dpeaa)DE-He213 Impulse noise detection (dpeaa)DE-He213 Impulse noise cancellation (dpeaa)DE-He213 Ibrahim, Haidi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 8(2013), 2 vom: 24. Aug., Seite 223-242 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:8 year:2013 number:2 day:24 month:08 pages:223-242 https://dx.doi.org/10.1007/s11760-013-0538-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2013 2 24 08 223-242
spelling	10.1007/s11760-013-0538-y doi (DE-627)SPR022267840 (SPR)s11760-013-0538-y-e DE-627 ger DE-627 rakwb eng Teoh, Sin Hoong verfasserin aut Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. Digital image processing (dpeaa)DE-He213 Impulse noise (dpeaa)DE-He213 Salt-and-pepper noise (dpeaa)DE-He213 Switching median filter (dpeaa)DE-He213 Impulse noise detection (dpeaa)DE-He213 Impulse noise cancellation (dpeaa)DE-He213 Ibrahim, Haidi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 8(2013), 2 vom: 24. Aug., Seite 223-242 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:8 year:2013 number:2 day:24 month:08 pages:223-242 https://dx.doi.org/10.1007/s11760-013-0538-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2013 2 24 08 223-242
allfields_unstemmed	10.1007/s11760-013-0538-y doi (DE-627)SPR022267840 (SPR)s11760-013-0538-y-e DE-627 ger DE-627 rakwb eng Teoh, Sin Hoong verfasserin aut Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. Digital image processing (dpeaa)DE-He213 Impulse noise (dpeaa)DE-He213 Salt-and-pepper noise (dpeaa)DE-He213 Switching median filter (dpeaa)DE-He213 Impulse noise detection (dpeaa)DE-He213 Impulse noise cancellation (dpeaa)DE-He213 Ibrahim, Haidi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 8(2013), 2 vom: 24. Aug., Seite 223-242 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:8 year:2013 number:2 day:24 month:08 pages:223-242 https://dx.doi.org/10.1007/s11760-013-0538-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2013 2 24 08 223-242
allfieldsGer	10.1007/s11760-013-0538-y doi (DE-627)SPR022267840 (SPR)s11760-013-0538-y-e DE-627 ger DE-627 rakwb eng Teoh, Sin Hoong verfasserin aut Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. Digital image processing (dpeaa)DE-He213 Impulse noise (dpeaa)DE-He213 Salt-and-pepper noise (dpeaa)DE-He213 Switching median filter (dpeaa)DE-He213 Impulse noise detection (dpeaa)DE-He213 Impulse noise cancellation (dpeaa)DE-He213 Ibrahim, Haidi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 8(2013), 2 vom: 24. Aug., Seite 223-242 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:8 year:2013 number:2 day:24 month:08 pages:223-242 https://dx.doi.org/10.1007/s11760-013-0538-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2013 2 24 08 223-242
allfieldsSound	10.1007/s11760-013-0538-y doi (DE-627)SPR022267840 (SPR)s11760-013-0538-y-e DE-627 ger DE-627 rakwb eng Teoh, Sin Hoong verfasserin aut Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2013 Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. Digital image processing (dpeaa)DE-He213 Impulse noise (dpeaa)DE-He213 Salt-and-pepper noise (dpeaa)DE-He213 Switching median filter (dpeaa)DE-He213 Impulse noise detection (dpeaa)DE-He213 Impulse noise cancellation (dpeaa)DE-He213 Ibrahim, Haidi aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 8(2013), 2 vom: 24. Aug., Seite 223-242 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:8 year:2013 number:2 day:24 month:08 pages:223-242 https://dx.doi.org/10.1007/s11760-013-0538-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2013 2 24 08 223-242
language	English
source	Enthalten in Signal, image and video processing 8(2013), 2 vom: 24. Aug., Seite 223-242 volume:8 year:2013 number:2 day:24 month:08 pages:223-242
sourceStr	Enthalten in Signal, image and video processing 8(2013), 2 vom: 24. Aug., Seite 223-242 volume:8 year:2013 number:2 day:24 month:08 pages:223-242
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Digital image processing Impulse noise Salt-and-pepper noise Switching median filter Impulse noise detection Impulse noise cancellation
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Teoh, Sin Hoong @@aut@@ Ibrahim, Haidi @@aut@@
publishDateDaySort_date	2013-08-24T00:00:00Z
hierarchy_top_id	546899102
id	SPR022267840
language_de	englisch
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author	Teoh, Sin Hoong
spellingShingle	Teoh, Sin Hoong misc Digital image processing misc Impulse noise misc Salt-and-pepper noise misc Switching median filter misc Impulse noise detection misc Impulse noise cancellation Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
authorStr	Teoh, Sin Hoong
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topic_title	Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies Digital image processing (dpeaa)DE-He213 Impulse noise (dpeaa)DE-He213 Salt-and-pepper noise (dpeaa)DE-He213 Switching median filter (dpeaa)DE-He213 Impulse noise detection (dpeaa)DE-He213 Impulse noise cancellation (dpeaa)DE-He213
topic	misc Digital image processing misc Impulse noise misc Salt-and-pepper noise misc Switching median filter misc Impulse noise detection misc Impulse noise cancellation
topic_unstemmed	misc Digital image processing misc Impulse noise misc Salt-and-pepper noise misc Switching median filter misc Impulse noise detection misc Impulse noise cancellation
topic_browse	misc Digital image processing misc Impulse noise misc Salt-and-pepper noise misc Switching median filter misc Impulse noise detection misc Impulse noise cancellation
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
ctrlnum	(DE-627)SPR022267840 (SPR)s11760-013-0538-y-e
title_full	Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
author_sort	Teoh, Sin Hoong
journal	Signal, image and video processing
journalStr	Signal, image and video processing
lang_code	eng
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author_browse	Teoh, Sin Hoong Ibrahim, Haidi
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author-letter	Teoh, Sin Hoong
doi_str_mv	10.1007/s11760-013-0538-y
title_sort	robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
title_auth	Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
abstract	Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. © Springer-Verlag London 2013
abstractGer	Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. © Springer-Verlag London 2013
abstract_unstemmed	Abstract In this manuscript, a new algorithm to reduce impulse noise from digital images has been proposed. This algorithm is based on switching median filtering approach, and therefore, it can be generally divided into two main stages; impulse noise detection stage and impulse noise cancellation stage. Modifications towards a well-known boundary discriminative noise detection method have been made. First, rather than using any sorting algorithm, we determine the local median values from manipulated local histograms. This solution makes the execution of the algorithm faster. Next, in the noise detection stage, in addition to the originally proposed intensity distance differential approach, the new method includes intensity height differential approach to reduce false detection rate. Then, instead of using adaptive approach in noise cancellation stage, our approach uses iterative approach, which has better local content preservation ability. Broad impulse noise model has been employed in this experiment. Based on the evaluations from root mean square error, false positive detection rate, false negative detection rate, mean structure similarity index, processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the-art median filtering techniques. © Springer-Verlag London 2013
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title_short	Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies
url	https://dx.doi.org/10.1007/s11760-013-0538-y
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author2	Ibrahim, Haidi
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doi_str	10.1007/s11760-013-0538-y
up_date	2024-07-04T02:29:25.537Z
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Robust algorithm for broad impulse noise removal utilizing intensity distance and intensity height methodologies

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