Download pdf
Comparison of processing results of median filter and mean filter on Gaussian noise
- 1 School of Central South University of Forestry and Technology, Changsha, Hunan 410000, China.
* Author to whom correspondence should be addressed.
Abstract
In people's daily lives, if the image is polluted with noise, it will become very blurred, so it is pretty necessary to use filtering to denoise the image and get a clearer image to assist people in work and study. The purpose of this article is by comparing the denoising result of the Median filter and Mean filter on Gaussian noise, and the filtering method which is more suitable for reducing Gaussian noise is found. Matlab is a very accurate and reliable scientific calculation standard software tool, which is very common in people's lives. The Median filter is a kind of nonlinear filter. It is so virtual at decreasing impulse noise. The common basic theory of the Median filter is to supersede the gray value of pixels with the median of the gray value in a neighborhood of the pixels, and not use the average proportion. The Mean filter is a sort of plain sliding-window space filter that takes the place of the central value with the mean of all the pixel values in the plain window. This essay takes advantage of Matlab to complete the process of adding Gaussian noise in photos, reducing Gaussian noise, and calculating the PSNR. The processing result of two types of noise reducers on Gaussian noise is compared. And by observing the image clarity, PSNR and MSE evaluation techniques to find which is a better filter to decrease Gaussian noise.
Keywords
Median Filter, Mean Filter, Gaussian Noise.
[1]. Isnanto R, Windarto Y, and Mangkuratmaja M 2020, Assessment on Image Quality Changes as a Result of Implementing Median filtering, Wiener Filtering, Histogram Equalization, and Hybrid Methods on Noisy Images, 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 2020, pp. 185-190
[2]. Sun S, Wang S 2009, Algorithm of improved extremum and median value filter, Journal of Computer Science, 36 ( 6 ): 165-166
[3]. Byungjoo C 2011, Noise value differences of DSLR video between editing programs after rendering process, IEEE/SICE International Symposium on System Integration (SII), 12/2011
[4]. Zhang Z 2001, Modern image processing and Matlab implementation[M]. Beijing: People’s posts and telecommunications press, pp. 171-201
[5]. Wang J, WangS 2011, Comparison of image denoising methods based on Matlab software[J]. Journal of Gansu Agricultural University, 46 ( 4 ): 157-160. DOI:10.3969/j. issn. 1003-4315. 2011. 04. 028
[6]. Keshijiandaoshule 2020, Gaussian blur and Gaussian noise, 2022.10.11, https://blog.csdn.net/u010440456/artical/details/109966520
[7]. Ding Ming. "Preserving Median Filtering Algorithm in Chip Images", The Open Electrical & Electronic Engineering Journal, 2014
[8]. Zhang Haohui. A Novel Algorithm for Image De-noise Based on Median filter and Wavelet Transform[J]. Electronic Technology, 2012, 25 ( 12 ): 40-41,43
[9]. Feng Liu, Xuehu Yan, Yuliang Lu. "A real-time destructive method to counter information hiding", 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), 2019
[10]. Banhaochunshui 2022, Mean filtering, 2022. 10. 11, https://blog.csdn.net/weixin_51571728/article/details/121455266
Cite this article
Sun,M. (2023). Comparison of processing results of median filter and mean filter on Gaussian noise. Applied and Computational Engineering,5,779-785.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).