An Improved Hybrid Median-Mean Filter Algorithm

Article Preview

Abstract:

In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

288-292

Citation:

Online since:

December 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] HengZ, Zhihui L, Xiaohua D. An Improved Method of Median Filter [J]. Journal of Image and Graphics, 2004, 9(4): 408-411.

Google Scholar

[2] Xiuqin D, Yong X, Hong P. Effective adaptive weighted median filter algorithm [J]. Computer Engineering and Applications, 2009, 45(35): 185-187.

Google Scholar

[3] Shihu Z, Chunxia Y. A Modified Average Filtering Algorithm [J]. Computer Applications and Software, 2013, 30(12): 97-100.

Google Scholar

[4] Yu Z, Xiqin W, Yingning P. Adaptive Center Weighted Modified Trimmed Mean Filter[J]. Journal of Tsinghua University(Science and Technology), 1999, 39(9): 76-78.

Google Scholar

[5] Qian X, Guangya H. Optimal Algorithm of Edge Defection Based on Median and Mean Filter [J]. Journal of Jishou University (Natural Science Edition), 2014, 35(1): 53-57.

Google Scholar

[6] Gonzalez. Digital Image Processing [M]. Beijing: Electronic Industry Press, 2005: 187-201.

Google Scholar

[7] Xiaoqiao H, Junsheng S, Jian Y, Juncai Y. Study on Color Image Quality Evaluation by MSE and PSNR Based on Color Difference[J]. Acta Photonica Sinica, 2007, 36(6): 4257-4259.

Google Scholar

[8] Yubing T, Qishan Z, Yunping Q. Image Quality Assessing by Combining PSNR with SSIM[J]. Journal of Image and Graphics,2006, 11(12): 1758-1763.

Google Scholar

[9] Keiichiro Kagawa, Koutaro Yasuoka, David C. Ng, Pulse-domain digital image processing for vision chips employing low-voltage operation in deep-submicrometer technologies [J]. IEEE Journal of Select Topics in Quantum Electronic, 2004, 10(4): 816-828.

DOI: 10.1109/jstqe.2004.833888

Google Scholar