Browse > Article
http://dx.doi.org/10.9728/dcs.2017.18.1.175

An Iterative Weighted Mean Filter for Mixed Noise Reduction  

Lee, Jung-Moon (Division of Electrical and Electronic Engineering, Kangwon National University)
Publication Information
Journal of Digital Contents Society / v.18, no.1, 2017 , pp. 175-182 More about this Journal
Abstract
Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.
Keywords
Median Filter; Mixed Noise; Noise Reduction; Sliding Window; Weighted Mean Filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. Hwang and R. A. Hadded, "Adaptive median filter : New algorithms and results," IEEE Trans. Image Process., Vol. 4, No. 4, pp. 499-502, Apr. 1995.   DOI
2 P. E. Ng and K. K. Ma, "A switching median filter with boundary discriminative noise detection for extremely corrupted images," IEEE Trans. Image Process., Vol. 15, No. 6, pp. 1506-1516, June 2006.   DOI
3 S. Esakkirajan, T. Veerakumar, A. N. Subramanyam, and C. H. PremChand, "Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter," IEEE Trans. Signal Process. Lett., Vol. 18, No. 5, May 2011.
4 C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," Proc. Int. Conf. Computer Vision, pp. 839-846, 1998.
5 M. Zhang and B. K. Gunturk, "Multiresolution bilateral filtering for image denoising," IEEE Trans. Image Process., Vol. 17, No. 12, pp. 2324-2333, Dec. 2008.   DOI
6 A. Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Vol. 2, pp. 60-65, 2005.
7 G. Treece, "The bitonic filter : Linear filtering in an edge-preserving morphological framework," IEEE Trans. Image Processing, Vol. 25, No. 11, Nov. 2016.
8 Jiahui Wang and Jingxin Hong, "A new self-adaptive weighted filter for removing noise in infrared images," 2009 International Conference on Information Engineering and Computer Science, 2009.
9 P. Zhang and F. Li, "A new adaptive weighted mean filter for removing salt-and-pepper noise," IEEE Signal Process. Lett., Vol. 21, No. 10, pp. 1280-1283, Oct. 2014.   DOI
10 P. Lin, B. Chen, F. Cheng, S. Huang, "A morphological mean filter for impulse noise removal," J. Diaplay Technology, Vol. 12, No. 4, April 2016.