An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise
This paper presents a novel algorithm to filter impulsive noise at very high noise density ( ≥ 85 % ). The proposed algorithm initially makes an accurate decision and selects a window which has sufficient information for denoising. Within the selected window, the proposed algorithm computes maximum,...
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| Published in | Wireless personal communications Vol. 119; no. 3; pp. 1975 - 1992 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.08.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0929-6212 1572-834X |
| DOI | 10.1007/s11277-021-08314-5 |
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| Abstract | This paper presents a novel algorithm to filter impulsive noise at very high noise density (
≥
85
%
). The proposed algorithm initially makes an accurate decision and selects a window which has sufficient information for denoising. Within the selected window, the proposed algorithm computes maximum, minimum, middle values along with their weights to restore noisy pixel. The performance of proposed filter is evaluated on natural and medical images with varying noise density. The proposed filter showed tremendous performance at high noise densities in terms of quantitative metrics and visual representation. Even at noise densities as high as 97% and 99%, the proposed filter is able to retrieve the details of the image. The proposed filter on an average improves the peak signal to noise ratio value by 10% in medical images over the existing. |
|---|---|
| AbstractList | This paper presents a novel algorithm to filter impulsive noise at very high noise density (
≥
85
%
). The proposed algorithm initially makes an accurate decision and selects a window which has sufficient information for denoising. Within the selected window, the proposed algorithm computes maximum, minimum, middle values along with their weights to restore noisy pixel. The performance of proposed filter is evaluated on natural and medical images with varying noise density. The proposed filter showed tremendous performance at high noise densities in terms of quantitative metrics and visual representation. Even at noise densities as high as 97% and 99%, the proposed filter is able to retrieve the details of the image. The proposed filter on an average improves the peak signal to noise ratio value by 10% in medical images over the existing. This paper presents a novel algorithm to filter impulsive noise at very high noise density (≥85%). The proposed algorithm initially makes an accurate decision and selects a window which has sufficient information for denoising. Within the selected window, the proposed algorithm computes maximum, minimum, middle values along with their weights to restore noisy pixel. The performance of proposed filter is evaluated on natural and medical images with varying noise density. The proposed filter showed tremendous performance at high noise densities in terms of quantitative metrics and visual representation. Even at noise densities as high as 97% and 99%, the proposed filter is able to retrieve the details of the image. The proposed filter on an average improves the peak signal to noise ratio value by 10% in medical images over the existing. |
| Author | Sharma, Nikhil Sohi, Prateek Jeet Singh Garg, Bharat |
| Author_xml | – sequence: 1 givenname: Nikhil surname: Sharma fullname: Sharma, Nikhil organization: Thapar Institute of Engineering and Technology, Patiala – sequence: 2 givenname: Prateek Jeet Singh surname: Sohi fullname: Sohi, Prateek Jeet Singh organization: Thapar Institute of Engineering and Technology, Patiala – sequence: 3 givenname: Bharat orcidid: 0000-0002-2904-3720 surname: Garg fullname: Garg, Bharat email: bharat.garg@thapar.edu organization: Thapar Institute of Engineering and Technology, Patiala |
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| Keywords | Impulse noise Mean filter Image processing Salt and pepper noise Median filters |
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| References | VijaykumarVMariGSEbenezerDFast switching based median-mean filter for high density salt and pepper noise removalAEU-International Journal of Electronics and Communications201468121145115510.1016/j.aeue.2014.06.002 EsakkirajanSVeerakumarTSubramanyamANPremChandCRemoval of high density salt and pepper noise through modified decision based unsymmetric trimmed median filterIEEE Signal Processing Letters201118528729010.1109/LSP.2011.2122333 ChenJLiFDenoising convolutional neural network with mask for salt and pepper noiseIET Image Processing201913132604261310.1049/iet-ipr.2019.0096 BhadouriaVSGhoshalDSiddiqiAHA new approach for high density saturated impulse noise removal using decision-based coupled window median filterSignal, Image and Video Processing201481718410.1007/s11760-013-0487-5 SattiPSharmaNGargBMin–Max average pooling based filter for impulse noise removalIEEE Signal Processing Letters2020271475147910.1109/LSP.2020.3016868 LuC-TChenY-YWangL-LChangC-FRemoval of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size windowPattern Recognition Letters20168018819910.1016/j.patrec.2016.06.026 MuruganKArunachalamVKarthikSHybrid filtering approach for retrieval of MRI imageJournal of Medical Systems2019431910.1007/s10916-018-1124-1 Aiswarya, K., Jayaraj, V., & Ebenezer, D. (2010). A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. In 2010 second international conference on computer modeling and simulation (Vol. 4). IEEE, pp. 409–413. RamachandranVKishorebabuVA tri-state filter for the removal of salt and pepper noise in mammogram imagesJournal of Medical Systems20194324010.1007/s10916-018-1133-0 GargBAryaKVFour stage median-average filter for healing high density salt and pepper noise corrupted imagesMultimedia Tools and Applications20207943323053232910.1007/s11042-020-09557-3 Arora, S., Hanmandlu, M., & Gupta, G. (2018). Filtering impulse noise in medical images using information sets. Pattern Recognition Letters. VeerakumarTEsakkirajanSVennilaIRecursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noiseSignal, Image and Video Processing20148115916810.1007/s11760-013-0517-3 Erkan, U.(2018).Different applied median filter in salt and pepper noise. Computers and Electrical Engineering, 70, 789–798. NgP-EMaK-KA switching median filter with boundary discriminative noise detection for extremely corrupted imagesIEEE Transactions on Image Processing20061561506151610.1109/TIP.2005.871129 Sohi, P. J. S., Sharma, N., Garg, B., & Arya, K. Noise density range sensitive mean-median filter for impulse noise removal. In Innovations in computational intelligence and computer vision (pp. 150–162). Springer. HwangHHaddadRAAdaptive median filters: New algorithms and resultsIEEE Transactions on Image Processing19954449950210.1109/83.370679 FaragallahOSIbrahemHMAdaptive switching weighted median filter framework for suppressing salt-and-pepper noiseAEU-International Journal of Electronics and Communications20167081034104010.1016/j.aeue.2016.04.018 Pitas, I., & Venetsanopoulos, A. N. (2013). Nonlinear digital filters: Principles and applications (Vol. 84). Springer. ZhangSKarimMAA new impulse detector for switching median filtersIEEE Signal Processing Letters200291136036310.1109/LSP.2002.805310 LiZLiuGXuYChengYModified directional weighted filter for removal of salt and pepper noisePattern Recognition Letters20144011312010.1016/j.patrec.2013.12.022 GargBAn adaptive minimum–maximum value-based weighted median filter for removing high density salt and pepper noise in medical imagesInternational Journal of Ad Hoc and Ubiquitous Computing2020352849510.1504/IJAHUC.2020.109795 GargBRestoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filterSignal, Image and Video Processing2020141555156310.1007/s11760-020-01695-3 ThanhDNHHienNNPrasathSAdaptive total variation L1 regularization for salt and pepper image denoisingOptik202020816367710.1016/j.ijleo.2019.163677 AstolaJKuosmaneenPFundamentals of nonlinear digital filtering1997Boca Raton, FLCRC K Murugan (8314_CR9) 2019; 43 8314_CR7 J Chen (8314_CR17) 2019; 13 H Hwang (8314_CR10) 1995; 4 8314_CR2 B Garg (8314_CR19) 2020; 79 OS Faragallah (8314_CR15) 2016; 70 VS Bhadouria (8314_CR11) 2014; 8 8314_CR13 8314_CR24 P Satti (8314_CR21) 2020; 27 C-T Lu (8314_CR23) 2016; 80 V Vijaykumar (8314_CR12) 2014; 68 8314_CR18 J Astola (8314_CR1) 1997 P-E Ng (8314_CR4) 2006; 15 T Veerakumar (8314_CR14) 2014; 8 DNH Thanh (8314_CR16) 2020; 208 B Garg (8314_CR20) 2020; 14 V Ramachandran (8314_CR8) 2019; 43 Z Li (8314_CR6) 2014; 40 S Zhang (8314_CR3) 2002; 9 S Esakkirajan (8314_CR5) 2011; 18 B Garg (8314_CR22) 2020; 35 |
| References_xml | – reference: GargBRestoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filterSignal, Image and Video Processing2020141555156310.1007/s11760-020-01695-3 – reference: MuruganKArunachalamVKarthikSHybrid filtering approach for retrieval of MRI imageJournal of Medical Systems2019431910.1007/s10916-018-1124-1 – reference: GargBAryaKVFour stage median-average filter for healing high density salt and pepper noise corrupted imagesMultimedia Tools and Applications20207943323053232910.1007/s11042-020-09557-3 – reference: VeerakumarTEsakkirajanSVennilaIRecursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noiseSignal, Image and Video Processing20148115916810.1007/s11760-013-0517-3 – reference: SattiPSharmaNGargBMin–Max average pooling based filter for impulse noise removalIEEE Signal Processing Letters2020271475147910.1109/LSP.2020.3016868 – reference: ZhangSKarimMAA new impulse detector for switching median filtersIEEE Signal Processing Letters200291136036310.1109/LSP.2002.805310 – reference: HwangHHaddadRAAdaptive median filters: New algorithms and resultsIEEE Transactions on Image Processing19954449950210.1109/83.370679 – reference: BhadouriaVSGhoshalDSiddiqiAHA new approach for high density saturated impulse noise removal using decision-based coupled window median filterSignal, Image and Video Processing201481718410.1007/s11760-013-0487-5 – reference: ThanhDNHHienNNPrasathSAdaptive total variation L1 regularization for salt and pepper image denoisingOptik202020816367710.1016/j.ijleo.2019.163677 – reference: RamachandranVKishorebabuVA tri-state filter for the removal of salt and pepper noise in mammogram imagesJournal of Medical Systems20194324010.1007/s10916-018-1133-0 – reference: ChenJLiFDenoising convolutional neural network with mask for salt and pepper noiseIET Image Processing201913132604261310.1049/iet-ipr.2019.0096 – reference: EsakkirajanSVeerakumarTSubramanyamANPremChandCRemoval of high density salt and pepper noise through modified decision based unsymmetric trimmed median filterIEEE Signal Processing Letters201118528729010.1109/LSP.2011.2122333 – reference: AstolaJKuosmaneenPFundamentals of nonlinear digital filtering1997Boca Raton, FLCRC – reference: NgP-EMaK-KA switching median filter with boundary discriminative noise detection for extremely corrupted imagesIEEE Transactions on Image Processing20061561506151610.1109/TIP.2005.871129 – reference: GargBAn adaptive minimum–maximum value-based weighted median filter for removing high density salt and pepper noise in medical imagesInternational Journal of Ad Hoc and Ubiquitous Computing2020352849510.1504/IJAHUC.2020.109795 – reference: VijaykumarVMariGSEbenezerDFast switching based median-mean filter for high density salt and pepper noise removalAEU-International Journal of Electronics and Communications201468121145115510.1016/j.aeue.2014.06.002 – reference: Pitas, I., & Venetsanopoulos, A. N. (2013). Nonlinear digital filters: Principles and applications (Vol. 84). Springer. – reference: FaragallahOSIbrahemHMAdaptive switching weighted median filter framework for suppressing salt-and-pepper noiseAEU-International Journal of Electronics and Communications20167081034104010.1016/j.aeue.2016.04.018 – reference: Sohi, P. J. S., Sharma, N., Garg, B., & Arya, K. Noise density range sensitive mean-median filter for impulse noise removal. In Innovations in computational intelligence and computer vision (pp. 150–162). Springer. – reference: LuC-TChenY-YWangL-LChangC-FRemoval of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size windowPattern Recognition Letters20168018819910.1016/j.patrec.2016.06.026 – reference: LiZLiuGXuYChengYModified directional weighted filter for removal of salt and pepper noisePattern Recognition Letters20144011312010.1016/j.patrec.2013.12.022 – reference: Aiswarya, K., Jayaraj, V., & Ebenezer, D. (2010). A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. In 2010 second international conference on computer modeling and simulation (Vol. 4). IEEE, pp. 409–413. – reference: Erkan, U.(2018).Different applied median filter in salt and pepper noise. Computers and Electrical Engineering, 70, 789–798. – reference: Arora, S., Hanmandlu, M., & Gupta, G. (2018). Filtering impulse noise in medical images using information sets. 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| SubjectTerms | Algorithms Communications Engineering Computer Communication Networks Density Engineering Image filters Medical imaging Networks Noise Signal to noise ratio Signal,Image and Speech Processing |
| Title | An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise |
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