INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.3, Mar. 2018

Fuzzy Entropy based Impulse Noise Detection and Correction Method for Digital Images

Full Text (PDF, 936KB), PP.36-46


Views:56   Downloads:1

Author(s)

S.Vijaya Kumar, C.Nagaraju

Index Terms

Fuzzy entropy;a window of interest;impulse noise;image restoration

Abstract

Impulse noise is the prime factor which reduces the quality of the digital image and it erases the important details of the images. De-noising is an indispensable task to restore the image features from the corrupted low- quality images and improve the perceptual quality of images. Several techniques are used for image quality enhancement and image restoration. In this work, an image de-noising scheme is developed to detect and correct the impulse noise from the image by using fuzzy entropy. The proposed algorithm is designed in two phases, such as noise detection phase, and correction phase. In the noise detection phase, the fuzzy entropy of pixels in a window of interest (WoI) is computed to detect whether the pixel is noisy or not.  The Fuzzy entropy of pixel greater than specified alpha cut value will be considered as noise pixel and submitted to correction phase. In the correction phase noise pixel value is replaced with a fuzzy weighted mean of the un-corrupted pixels in the WoI. The proposed Fuzzy entropy based impulse noise detection and correction method are implemented using MATLAB. The experimentation has been carried out on different standard images and the analysis is performed by comparing the performance of the proposed scheme with that of the existing methods such as  DBA, MDBUTMF, AMF, NAFSM, BDND, and CM , using PSNR, SSIM, and NAE as metric parameters. The proposed method will give good results compared to state of the art methods in image restoration.

Cite This Paper

S.Vijaya Kumar, C.Nagaraju," Fuzzy Entropy based Impulse Noise Detection and Correction Method for Digital Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.3, pp. 36-46, 2018.DOI: 10.5815/ijigsp.2018.03.05

Reference

[1]RC Gonzalez and RE Woods, "the processing of digital images," Prentice Hall, 2002. 

[2]A. I. pitas and Venetsanopou nonlinear, digital filters: Principles and Applications. Norwell, MA: Kluwer, 1990.

[3]J. Astola and P. Kuosmanen, non-linear digital filter concepts. Boca Raton, FL: CRC 1997.

[4]T. Sun and Y. Neuvo, "Detail preservation of median filters in the image processing," Recognit.Lett., 15, pp. 341-347 1994.

[5]D. Florencio and R. Schafer, "the inauguration median filter using local statistics signal," in Proc. SPIE Int. Symp. The image processing, visual communication, Chicago in September 1994.

[6]DRK Brownrigg, "the weighted median filter" ACM Commun., 27, No. 8, pp. 807-818, August 1984.

[7]Ko SJ and YH Lee, "center weighted median filters and their applications to improve the image," IEEE Trans. Syst. Circuits, vol. 38, no. 9, pp. 984-993, September 1991.

[8]H. Hwang and RA Haddad, "Adaptive Median Filters: new algorithms and results," IEEE Transactions on Image Processing, Vol.4, No. 4, April 1995. 

[9]DZ Wang and Zhang, "progressive switching median filter for the removal of impulse noise highly corrupted images," IEEE Trans Circuits. Syst. II, processing the analog signal. Figures. 46, no. 1, pp. 78-80, January 1999.

[10]S. Zhang and A. Karim, "A new pulse detector for switching median filters," IEEE Signal Process. Lett., 9, No. 11, pp. 360-363, November The year 2002.

[11]How-Lung Eng and Kai-KuangMa,”Noise adaptive soft-switching median filter”, IEEE Transactions on Image Processing,  Vol.10, No 2, August 2002.

[12]EP Ng KK and Ma, “A switching median filter with boundary discriminative noise detection for extremely corrupted images," IEEE Transactions on Image Processing, 15, No. 6, pp. 1506 - 1516 2006.

[13]KS Srinivasan and D. Ebenezer "a new fast and efficient decision based removal of high density impulse noise algorithm," IEEE Signal Processing Lett, 14, No. 3, pp.189. - 192, 2007.

[14]S.Esakkirajan,T.V.Kumar,Adabala .Subramanyam And C.H.Prechand “Removal Of High Density Salt And Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter” Ieee Signal Processing Letters, Vol. 18, No. 5, May 2011 .

[15]Y. Li DY and Artificial Intelligence of the uncertainty, 2007: CRC Press.

[16]Zhe Zhou, "Cognition and removal of impulse noise with uncertainty," IEEE Transactions on Image processing, 21, No. 7, pp. 3157-3167, July 2012.

[17]A. De Luca, S. Termini, “A definition of non-probabilistic entropy in the setting of fuzzy set theory,” Information and Control, vol.20, no.4, pp. 301-312, 1972.

[18]G.J. Klir, T.A. Folger, Fuzzy Sets, Uncertainty, and Information, in: Uncertainty and Information, Prentice-Hall International Editions, 1988.

[19]L. Xuecheng, “Entropy, distance measure and similarity measure of fuzzy sets and their relations”, Fuzzy Sets and Systems, vol. 52, pp. 305-318, 1992. 

[20]D. Bhandari and N.R. Pal, “Some new information measure of fuzzy sets”, Inform. Sci. vol. 67, pp. 209–228, 1993

[21]H. T Nguyen, “Fuzzy sets and probability”, Fuzzy Sets and Systems, vol. 90, pp. 129-132, 1997

[22]H.-M. Lee, C.-M. Chen, J.-M. Chen, and Y.-L. Jou, “An efficient fuzzy classifier with feature selection based on fuzzy entropy,” IEEE Trans. On Systems, Man, and Cybernetics-part B: Cybernetics, Vol. 31, No. 3, 2001, pp. 426-432.

[23]SemaKocKayhan, "A phase 2 method to remove impulse noise in images," J. VisCommun. ImageR, Vol 25, pp.478-486, 2014.

[24]PS Windyga "fast, suppression of impulsive noise," IEEE Trans. Process the image, Vol 10, no. 1, pp. 173-179, January 2001.

[25]I. and C. Butakoff Aizenberg, "effective pulse based on sensors sorting criteria," IEEE Signal Process. Lett., 11, No. 3, pp. 363-366, March 2004.

[26]Vijaya Kumar and C.Nagaraju,” Identifying and Removal of Impulse Noise with Fuzzy Certainty Degree” IEEE international Conference on Communications and Electronics systems, 2016

[27]Wu Qiu,Feng xiao,Xin Yang,Xuming Zhang,Ming Yuchi,Mingyue Ding,"Research on Fuzzy Enhancement in the Diagnosis of Liver Tumor from B-mode Ultrasound Images", IJIGSP, vol.3, no.3, pp.10-16, 2011.

[28]K. Kannan,"A new Decision Based Median Filter using Cloud Model for the removal of high density Salt and Pepper noise in digital color images", IJIGSP, vol.6, no.4, pp.46-53, 2014

[29]Hani M. Ibrahem,"An Efficient Switching Filter Based on Cubic B-Spline for Removal of Salt-and-Pepper Noise", IJIGSP, vol.6, no.5, pp.45-52, 2014.

[30]Rupinder Kaur, Raman Maini,"Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.8, No.7, pp.9-21, 2016.