TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Adaptive fuzzy inference system based directional median filter for impulse noise removal

Tutkimustuotosvertaisarvioitu

Standard

Adaptive fuzzy inference system based directional median filter for impulse noise removal. / Habib, Muhammad; Hussain, Ayyaz; Rasheed, Saqib; Ali, Mubashir.

julkaisussa: AEU International Journal of Electronics and Communication, Vuosikerta 70, Nro 5, 01.05.2016, s. 689-697.

Tutkimustuotosvertaisarvioitu

Harvard

Habib, M, Hussain, A, Rasheed, S & Ali, M 2016, 'Adaptive fuzzy inference system based directional median filter for impulse noise removal', AEU International Journal of Electronics and Communication, Vuosikerta. 70, Nro 5, Sivut 689-697. https://doi.org/10.1016/j.aeue.2016.02.005

APA

Habib, M., Hussain, A., Rasheed, S., & Ali, M. (2016). Adaptive fuzzy inference system based directional median filter for impulse noise removal. AEU International Journal of Electronics and Communication, 70(5), 689-697. https://doi.org/10.1016/j.aeue.2016.02.005

Vancouver

Habib M, Hussain A, Rasheed S, Ali M. Adaptive fuzzy inference system based directional median filter for impulse noise removal. AEU International Journal of Electronics and Communication. 2016 touko 1;70(5):689-697. https://doi.org/10.1016/j.aeue.2016.02.005

Author

Habib, Muhammad ; Hussain, Ayyaz ; Rasheed, Saqib ; Ali, Mubashir. / Adaptive fuzzy inference system based directional median filter for impulse noise removal. Julkaisussa: AEU International Journal of Electronics and Communication. 2016 ; Vuosikerta 70, Nro 5. Sivut 689-697.

Bibtex - Lataa

@article{b73e312eb0384e05bb68077ae55818c3,
title = "Adaptive fuzzy inference system based directional median filter for impulse noise removal",
abstract = "Noise filtering in presence of important image detail information is considered as challenging task in imaging applications. Use of fuzzy logic based techniques is capturing more focus since last decade to deal with these challenges. In order to tackle conflicting issues of noise smoothing and detail preservation, this paper presents a novel approach using adaptive fuzzy inference system for random valued impulse noise detection and removal. The proposed filter uses the intensity based directional statistics to construct adaptive fuzzy membership functions which plays an important role in fuzzy inference system. Fuzzy inference system constructed in this way is used by the noise detector for accurate classification of noisy and noise-free pixels by differentiating them from edges and detailed information present in an image. After classification of pixels, noise adaptive filtering is performed based on median and directional median filter using the information provided by the noise detector. Simulation results based on well known quantitative measure i.e., peak-signal-to-noise ratio (PSNR) show the effectiveness of proposed filter.",
keywords = "Adaptive threshold, Fuzzy inference system, Noise detection, Noise removal, Random-valued impulse noise",
author = "Muhammad Habib and Ayyaz Hussain and Saqib Rasheed and Mubashir Ali",
note = "INT=elt,{"}Ali, Mubashir{"}",
year = "2016",
month = "5",
day = "1",
doi = "10.1016/j.aeue.2016.02.005",
language = "English",
volume = "70",
pages = "689--697",
journal = "AEU International Journal of Electronics and Communication",
issn = "1434-8411",
publisher = "Elsevier",
number = "5",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Adaptive fuzzy inference system based directional median filter for impulse noise removal

AU - Habib, Muhammad

AU - Hussain, Ayyaz

AU - Rasheed, Saqib

AU - Ali, Mubashir

N1 - INT=elt,"Ali, Mubashir"

PY - 2016/5/1

Y1 - 2016/5/1

N2 - Noise filtering in presence of important image detail information is considered as challenging task in imaging applications. Use of fuzzy logic based techniques is capturing more focus since last decade to deal with these challenges. In order to tackle conflicting issues of noise smoothing and detail preservation, this paper presents a novel approach using adaptive fuzzy inference system for random valued impulse noise detection and removal. The proposed filter uses the intensity based directional statistics to construct adaptive fuzzy membership functions which plays an important role in fuzzy inference system. Fuzzy inference system constructed in this way is used by the noise detector for accurate classification of noisy and noise-free pixels by differentiating them from edges and detailed information present in an image. After classification of pixels, noise adaptive filtering is performed based on median and directional median filter using the information provided by the noise detector. Simulation results based on well known quantitative measure i.e., peak-signal-to-noise ratio (PSNR) show the effectiveness of proposed filter.

AB - Noise filtering in presence of important image detail information is considered as challenging task in imaging applications. Use of fuzzy logic based techniques is capturing more focus since last decade to deal with these challenges. In order to tackle conflicting issues of noise smoothing and detail preservation, this paper presents a novel approach using adaptive fuzzy inference system for random valued impulse noise detection and removal. The proposed filter uses the intensity based directional statistics to construct adaptive fuzzy membership functions which plays an important role in fuzzy inference system. Fuzzy inference system constructed in this way is used by the noise detector for accurate classification of noisy and noise-free pixels by differentiating them from edges and detailed information present in an image. After classification of pixels, noise adaptive filtering is performed based on median and directional median filter using the information provided by the noise detector. Simulation results based on well known quantitative measure i.e., peak-signal-to-noise ratio (PSNR) show the effectiveness of proposed filter.

KW - Adaptive threshold

KW - Fuzzy inference system

KW - Noise detection

KW - Noise removal

KW - Random-valued impulse noise

U2 - 10.1016/j.aeue.2016.02.005

DO - 10.1016/j.aeue.2016.02.005

M3 - Article

VL - 70

SP - 689

EP - 697

JO - AEU International Journal of Electronics and Communication

JF - AEU International Journal of Electronics and Communication

SN - 1434-8411

IS - 5

ER -