Friday 29th of March 2024
 

Removal of High Density Salt and Pepper Noise Through Super Mean Filter for Natural Images


Shyam Lal, Sanjeev Kumar and Mahesh Chandra

A super-mean filter (SUMF) is proposed to remove high density salt pepper noise from digital images. The proposed filter works in two stages, in the first stage the noisy pixels are detected and in the second stage each noisy pixel is replaced by the mean value of noise free pixel of 22 matrix. Extensive simulation and experimental results shows that the proposed filter works well consistently for suppressing the salt pepper noise. The performance of proposed filter is compared with the other existing filters, standard median filter(SMF), centre weighted median filter (CWMF), progressive switching median filter (PSMF), open-close sequence filter (OCSF), decision based algorithm (DBA), modified decision based unsymmetric trimmed median filter (MDBUTMF). The proposed filter shows better performance as compared to above mentioned filters for noise removal from different gray scale images.

Keywords: : Super mean filter, Median filter, Centre weighted median filter, Open-close sequence filter, Decision based algorithm, Modified decision based unsymmetric trimmed median filter.

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ABOUT THE AUTHORS

Shyam Lal
Shyam Lal received B.Tech. (with Hons.) in Electronics & Communication Engineering from Bundelkhand Institute of Engineering & Technology (B.I.E.T) Jhansi (U.P.), India and M.Tech.(with Hons.) in Electronics & Communication Engineering from National Institute of Technology, Kurukshetra (Haryana)-India in year 2001 and 2007, respectively . He is pursuing Ph.D. degree in the area of Digital Image Processing from Department of Electronics & Communication Engineering, Birla Institute of Technology, Mesra, Ranchi (Jharkhand),India. He has been Associate Professor in the department of Electronics & Communication Engineering, Moradabad Institute of Technology, Moradabad (U.P.), India. He has published more than 35 papers at International/National Journals & Conferences. He is Member of IEEE (Since 2011), life member of Indian Society of Technical Education (ISTE), New Delhi-India (LM-39989), International Association of Engineers (IAENG), Hong Kong (M.No.-103480) and Life member International Association of Computer Science and Information Technology (IACSIT), Singapore (LM- 80333445). He has more than eleven years of teaching experience. His area of interest includes Digital Image Processing, Digital Signal Processing and Wireless Communication.

Sanjeev Kumar
Sanjeev kumar received his B.Sc (Engg.) degree in Electronics and Communications from the Cambridge Institute of Technology, Ranchi (JH), India in 2009 and M.E. degree in Instrumentation & Control from Department of ECE, BIT, Mesra, Ranchi (JH), India 2012. Currently, he is Asstistant Professor at Cambridge Institute of Technology, Ranchi in Department of Electronics and Communications. His research interests include signal processing, image processing, neural networks, fuzzy systems and applications of these techniques.

Mahesh Chandra
Mahesh Chandra received B.Sc. from Agra University, Agra (U.P.)-India in 1990 and A.M.I.E. from I.E.I., Kolkata (W.B.)-India in winter 1994. He received M.Tech. from J.N.T.U., Hyderabad-India in 2000 and Ph.D. from AMU, Aligarh (U.P.)-India in 2008. He has worked as Reader & HOD in the Department of Electronics & Communication Engineering at S.R.M.S. College of Engineering and Technology, Bareilly (U.P.)-India from Jan 2000 to June 2005. He is presently working as Associate Professor in the Department of Electronics & Communication Engineering, B.I.T., Mesra, Ranchi (Jharkhand)-India. He is a Life Member of ISTE, New Delhi-India and Member of IEI Kolkata (W.B.)-India. He has published more than 32 research papers in the area of Speech, Signal and Image Processing at International/ National level conferences and journals. He is currently guiding 05 Ph.D. students in the area of Speech, Signal and Image Processing. His areas of interest are Speech, Signal and Image Processing.


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