Abstract
In this paper, we propose a neuro-fuzzy based blind image restoration to remove impulse noise from low as well as highly corrupted images. Main components of the proposed technique include noise detection, histogram estimation and noise filtering process. Proposed technique constructs the fuzzy sets using fuzzy number construction algorithm. These fuzzy sets are used in noise filtering process to remove impulse noise from the noisy pixels using neuro-fuzzy inference system and fuzzy decider. Experimental results are based on global and local error measures, which prove that the proposed technique gives superior results than the present well known impulse noise filtering methods.
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References
Arakawa K (1996) Median filter based on fuzzy rules and its application to image restoration. Fuzzy Set Syst 77:3–13
Castillo O, Melin P (2008) Intelligent systems with interval type-2 fuzzy logic. International Journal of Innovative Computing, Information and Control 4(4):771–784
Chaudhry A, Khan A, Mirza AM, Ali A (2006) A hybrid image restoration approach: using fuzzy punctual Kriging and genetic programming. International Journal of Imaging Systems and Technology, Wiley
Eng H-L, Ma K-K (2001) Noise adaptive soft-switching median filter. IEEE Transactions on Image Processing 10(2):242–251
Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall
Hussain A, Arfan Jaffar M, Mirza AM, Asmatullah Chaudhry, Detail preserving fuzzy filter for impulse noise removal. International Journal of Innovative Computing, Information and Control, 5, no.10, October 2009
Kaliraj G, Baskar S (2010) An efficient approach for the removal of impulse noise from the corrupted image using neural network based impulse detector. Image and Vision Computing 28:458–466
Lee C-S, Guo S-M, Hsu C-Y (2004) A Novel fuzzy filter for impulse noise removal. Lecture Notes in Computer Science 3174(2004):375–380
Lee C-S, Guo S-M, Hsu C-Y (2005) Genetic-based fuzzy image filter and its applications to image processing. IEEE Trans Syst, Man, Cybern 35(4):694–711
Mockor J (2008) Models of fuzzy logic in a category of sets with similarity relations. International Journal of Innovative Computing, Information and Control 4(5):1063–1068
Pei-Eng Ng, Kai-Kuang Ma, A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing 15, NO. 6, JUNE 2006
Ponomarchuk Y, Seo D-W (2010) Intrusion detection based on traffic analysis and fuzzy inference system in wireless sensor networks. JoC 1(1):35–42
Sathappan OL, Chitra P, Venkatesh P, Prabhu M (2011) Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system. IJITCC 1(2):146–158
Schulte S, Nachtegael M, de Witte V, Van der Weken D, Kerre EE (2006) A fuzzy impulse noise detection and reduction method. IEEE Trans Image Process 15(5):1153–1162
Standard Image Page [Online]. Available from: <http://www.sys.uea.ac.uk/Research/researchareas/imagevision/images_ftp/>
Standard Test Images—Lena, M. Wakin. [Online]. Available from: <http://www.ece.rice.edu/~/wakin/images/>
Wang JH, Liu WJ, Lin LD (2002) Histogram-based fuzzy filter for image restoration. IEEE Trans Syst, Man, Cybern 32(2):230–238
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2011–0006644).
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Masood, S., Hussain, A., Jaffar, M.A. et al. Intelligent noise detection and filtering using neuro-fuzzy system. Multimed Tools Appl 63, 93–105 (2013). https://doi.org/10.1007/s11042-012-1015-2
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DOI: https://doi.org/10.1007/s11042-012-1015-2