An efficient method for impulse noise reduction from images using fuzzy cellular automata

Abstract Impulse noise reduction from corrupted images plays an important role in image processing. This problem will also affect on image segmentation, object detection, edge detection, compression, etc. Generally, median filters or nonlinear filters have been used for noise reduction but these methods will destroy the natural texture and important information in the image like the edges. In this paper, to eliminate impulse noises from noisy images, we used a hybrid method based on cellular automata (CA) and fuzzy logic called Fuzzy Cellular Automata (FCA) in two steps. In the first step, based on statistical information, noisy pixels are detected by CA; then using this information, the noisy pixel will change by FCA. Regularly, CA is used for systems with simple components where the behavior of each component will be defined and updated based on its neighbors. The proposed hybrid method is characterized as simple, robust and parallel which keeps the important details of the image effectively. The proposed approach has been performed on well-known gray scale test images and compared with other conventional and famous algorithms, is more effective.

[1]  Fabrizio Russo,et al.  FIRE operators for image processing , 1999, Fuzzy Sets Syst..

[2]  Haidi Ibrahim,et al.  Salt-and-pepper noise detection and reduction using fuzzy switching median filter , 2008, IEEE Transactions on Consumer Electronics.

[3]  Mohammad Mehdi Ebadzadeh,et al.  A new method for impulse noise reduction from digital images Based on Adaptive Neuro-Fuzzy System and Fuzzy Wavelet Shrinkage , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[4]  Paul L. Rosin.  Image processing using 3-state cellular automata , 2010, Comput. Vis. Image Underst..

[5]  F. Russo,et al.  A fuzzy filter for images corrupted by impulse noise , 1996, IEEE Signal Processing Letters.

[6]  Sung-Jea Ko,et al.  Center weighted median filters and their applications to image enhancement , 1991 .

[7]  Wen-June Wang,et al.  Fuzzy reasoning-based directional median filter design , 2009, Signal Process..

[8]  Hans J. Herrmann,et al.  Cellular Automata for Elementary Image Enhancement , 1996, CVGIP Graph. Model. Image Process..

[9]  Rachid Harba,et al.  A New Adaptive Switching Median Filter , 2010, IEEE Signal Processing Letters.

[10]  Shuqun Zhang,et al.  A new impulse detector for switching median filters , 2002, IEEE Signal Processing Letters.

[11]  Ilke Turkmen,et al.  Efficient impulse noise detection method with ANFIS for accurate image restoration , 2011 .

[12]  Mohammad Bagher Menhaj,et al.  A Fuzzy Logic Control Based Approach for Image Filtering , 2000 .

[13]  Moncef Gabbouj,et al.  Weighted median filters: a tutorial , 1996 .

[14]  D. Ebenezer,et al.  Adaptive Window Length Recursive Weighted Median Filter for Removing Impulse Noise in Images with Details Preservation , 2007 .

[15]  M.R. Meybodi,et al.  Adaptive Edge Detection via Image Statistic Features and Hybrid Model of Fuzzy Cellular Automata and Cellular Learning Automata , 2009, 2009 International Conference on Information and Multimedia Technology.

[16]  K. Kannan,et al.  A hypergraph-based algorithm for image restoration from salt and pepper noise , 2010 .

[17]  N. Gallagher,et al.  An overview of median and stack filtering , 1992 .

[18]  Fangxia Guo,et al.  Impulsive noise detection by double noise detector and removal using adaptive neural-fuzzy inference system , 2011 .

[19]  Nor Ashidi Mat Isa,et al.  Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction , 2010, IEEE Signal Processing Letters.

[20]  Mohammad Mehdi Ebadzadeh,et al.  Enhancement of Ear Verification System Performance Using a New Hybrid Denoising Approach (ANFIS-FWS) , 2010, 2010 First International Conference on Integrated Intelligent Computing.

[21]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[22]  Eduardo Abreu,et al.  Signal-dependent rank-ordered-mean (SD-ROM) filter , 2000 .

[23]  Etienne E. Kerre,et al.  A fuzzy impulse noise detection and reduction method , 2006, IEEE Transactions on Image Processing.

[24]  Jian-Jun Zhang,et al.  An efficient median filter based method for removing random-valued impulse noise , 2010, Digit. Signal Process..

[25]  Parimal Pal Chaudhuri,et al.  Fuzzy Cellular Automata for Modeling Pattern Classifier , 2005, IEICE Trans. Inf. Syst..

[26]  M. Emre Celebi,et al.  Robust switching vector median filter for impulsive noise removal , 2008, J. Electronic Imaging.

[27]  Ivan Kalaykov,et al.  Real-time Image Noise Cancellation Based on Fuzzy Similarity , 2003 .

[28]  H. Wu,et al.  Adaptive impulse detection using center-weighted median filters , 2001, IEEE Signal Processing Letters.

[29]  K. Faez,et al.  A two-pass method to impulse noise reduction from digital images based on neural networks , 2008, 2008 International Conference on Electrical and Computer Engineering.

[30]  Ayyaz Hussain,et al.  Fuzzy based Impulse Noise Reduction Method , 2011, Multimedia Tools and Applications.

[31]  Chun-Ling Chang,et al.  Cellular automata for edge detection of images , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).