A Novel Median Filter to Iteratively Remove Salt-and-Pepper Noise from Highly Corrupted Images

LAN Xia; LIU Xinxin; SHEN Huanfeng; YUAN Qiangqiang; ZHANG Liangpei

doi:10.13203/j.whugis20150520

Volume 42 Issue 12

Dec. 2017

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2017 > 42(12): 1731-1737. > DOI: 10.13203/j.whugis20150520

LAN Xia, LIU Xinxin, SHEN Huanfeng, YUAN Qiangqiang, ZHANG Liangpei. A Novel Median Filter to Iteratively Remove Salt-and-Pepper Noise from Highly Corrupted Images[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1731-1737. DOI: 10.13203/j.whugis20150520

Citation:

PDF (1150 KB)

A Novel Median Filter to Iteratively Remove Salt-and-Pepper Noise from Highly Corrupted Images

LAN Xia^1,,
LIU Xinxin²,
SHEN Huanfeng^{2, 3, ,},
YUAN Qiangqiang^{3, 4},
ZHANG Liangpei^{3, 5}

1.
The 10 th Institute of China Electronics Technology Group Corporation, Chengdu 610036, China
2.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
3.
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
4.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
5.
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 41401383

The National Natural Science Foundation of China 41401396

More Information

Author Bio:
LAN Xia, PhD, specializes in signal and information processing. E-mail: lanxia2004@163.com
Corresponding author:
SHEN Huanfeng, PhD, professor. E-mail: shenhf@whu.edu.cn
Received Date: July 26, 2016
Published Date: December 04, 2017

Graphical Abstract

Abstract

Abstract

In this paper, we propose a simple but efficient filter to effectively remove salt-and-pepper noise from highly corrupted images inspired by the corresponding limitations of existing filtering methods. After ensuring the location of ill pixels based on their intensity value, our method then utilizes the iterative processing framework to gradually restore the noisy images. When the useful information of one corrupted image is much enough, the proposed method can refine the results through particular designed criterion. The experiments from standard test images show that the proposed method can better recover the detail information and maintain the optimal performances qualitatively and quantitatively in the comparisons. Even the ratio of salt-and-pepper noise is as high as 95%, the advantage of our filter is still significant.
- salt-and-pepper noise,
- median filter,
- iteration,
- noise removal,
- high density

FullText(HTML)

References (16)

References

[1]	Bovik A. Handbook of Image and Processing[M]. New York:Academic Press, 2000
[2]	Rosenfeld A, Woods R E. Digital Picture Processing[M]. New York:Academic Press, 1982
[3]	Nodes T, Gallagher N C. The Output Distribution of Median Type Filters[J]. IEEE Transactions on Communications, 1984, 32(5):532-541 doi: 10.1109/TCOM.1984.1096099
[4]	Brownrigg D. The Weighted Median Filter[J]. Communications of the ACM, 1984, 27(8):807-818 doi: 10.1145/358198.358222
[5]	Ko S J, Lee Y H. Center Weighted Median Filters and Their Applications to Image Enhancement[J]. IEEE Transactions on Circuits & Systems, 1991, 38(9):984-993 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=83870&contentType=Journals+%26+Magazines&punumber%3D31
[6]	Abreu E, Lightstone M, Mitra S K, et al. A New Efficient Approach for the Removal of Impulse Noise from Highly Corrupted Images[J]. IEEE Transactions on Image Processing, 1996, 5(6):1012-1025 doi: 10.1109/83.503916
[7]	Sun T, Neuvo Y. Detail-Preserving Median Based Filters in Image Processing[J]. Pattern Recognition Letters, 1994, 15(4):341-347 doi: 10.1016/0167-8655(94)90082-5
[8]	李树涛, 王耀南.图象椒盐噪声的非线性自适应滤除[J].中国图象图形学报, 2000, 5(12):999-1001 doi: 10.3969/j.issn.1006-8961.2000.12.004 Li Shutao, Wang Yaonan. Non-Linear Adaptive Removal of Salt and Pepper Noise from Images[J]. Journal of Image and Graphics, 2000, 5(12):999-1001 doi: 10.3969/j.issn.1006-8961.2000.12.004
[9]	邢藏菊, 王守觉, 邓浩江, 等.一种基于极值中值的新型滤波算法[J].中国图象图形学报, 2001, 6(6):533-536 http://www.cqvip.com/QK/90287X/2001006/5376853.html Xing Cangju, Wang Shoujue, Deng Haojiang, et al. A New Filtering Algorithm Based on Extremum and Median Value[J]. Journal of Image and Graphics, 2001, 6(6):533-536 http://www.cqvip.com/QK/90287X/2001006/5376853.html
[10]	Zhang S, Karim M A. A New Impulse Noise Detector for Switching Median Filters[J]. IEEE Signal Processing Letters, 2002, 9(11):360-363 doi: 10.1109/LSP.2002.805310
[11]	Chan R H, Ho C W, Nikolova M. Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail Preserving Regularization[J]. IEEE Transactions on Signal Processing, 2005, 14(10):1479-1485 http://ci.nii.ac.jp/naid/80017549968
[12]	Srinivasan K S, Ebenezer D. A New Fast and Efficient Decision-Based on Algorithm for Removal of High-Density Impulse Noise[J]. IEEE Signal Processing Letters, 2007, 14(3):189-192 doi: 10.1109/LSP.2006.884018
[13]	Ibrahim H, Kong N S P, Ng T F. Simple Adaptive Median Filter for the Removal of Impulse Noise from Highly Corrupted Images[J]. IEEE Transactions on Consumer Electronics, 2008, 54(4):1920-1927 doi: 10.1109/TCE.2008.4711254
[14]	Wang Z, Zhang D. Progressive Switching Median Filter for the Removal Impulse Noise from Highly Corrupted Images[J]. IEEE Transactions on Circuits and Systems Ⅱ Analog & Digital Signal Processing, 1999, 46(1):78-80 http://citeseerx.ist.psu.edu/showciting?cid=260222
[15]	Ahmed F, Das S. Removal of High-Density Salt-and-Pepper Noise in Images with an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(5):1352-1358 doi: 10.1109/TFUZZ.2013.2286634
[16]	Bhadouria V S, Ghoshal D, Siddiqi A H. A New Approach for High Density Saturated Impulse Noise Removal Using Decision-Based Coupled Window Median Filter[J]. Signal Image and Video Processing, 2014, 8(1):71-84 doi: 10.1007/s11760-013-0487-5

[1]	LI Da, QU Wei, ZHANG Qin, LI Jiuyuan, LING Qing. Landslide Displacement Prediction Model Integrating Multi-layer Perceptron and Optimized Support Vector Regression[J]. Geomatics and Information Science of Wuhan University, 2023, 48(8): 1380-1388. DOI: 10.13203/j.whugis20210703
[2]	ZHANG Yixiao, ZHAO Zhongguo, ZHENG Jianghua. Estimation of Reference Crop Evapotranspiration Under Different Combination of Meteorological Elements Using Multivariate Adaptive Regression Splines[J]. Geomatics and Information Science of Wuhan University, 2022, 47(5): 789-798. DOI: 10.13203/j.whugis20190337
[3]	XU Shan, ZOU Bin, WANG Min, LIU Ning. Performance Comparison of Artificial Neural Network and Kriging in Spatial Estimation of PM_2.5 Concentration[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1642-1650. DOI: 10.13203/j.whugis20180482
[4]	XU Yanfang, LIU Wenyao, WU Bing, HUANG Min. Application of Neural Network in Color Conversion[J]. Geomatics and Information Science of Wuhan University, 2006, 31(9): 799-801.
[5]	YU Liang, BIAN Fuling. Application of Rough Set-Based Neural Network in Forest Fire Alarm[J]. Geomatics and Information Science of Wuhan University, 2006, 31(8): 720-723.
[6]	HU Yaogai, LI Wei, HU Jiming. An Artificial Neural Network with Improved Activation Function and Its Application[J]. Geomatics and Information Science of Wuhan University, 2004, 29(10): 916-919.
[7]	SHAO Lixia, HE Zongyi, AI Zixing, SONG Xiaodong. Automatic Generalization of River Network Based on BP Neural Network Techniques[J]. Geomatics and Information Science of Wuhan University, 2004, 29(6): 555-557.
[8]	XIAO Ping, LI Deren. Land Use/Cover Change Detection Based on Artificial Neural Network[J]. Geomatics and Information Science of Wuhan University, 2002, 27(6): 586-590.
[9]	Shen Weiming, Zhang Zuxun, Zhang Jianqing. Image Matching by Neural Network Based Relaxation[J]. Geomatics and Information Science of Wuhan University, 1996, 21(3): 247-251.
[10]	Huang Wenqian. Recognition of Point-shaped Map Symbols with a New Neural Network Model[J]. Geomatics and Information Science of Wuhan University, 1996, 21(1): 46-49.

Cited By

Cited by

Periodical cited type(1)

吴静，傅优杰，程朋根. 基于粗糙集的局部同位模式挖掘算法. 测绘通报. 2022(10): 80-85+104 .

Other cited types(3)

Get Citation

PDF

XML

Article views (1891) PDF downloads (406) Cited by(4)

A Novel Median Filter to Iteratively Remove Salt-and-Pepper Noise from Highly Corrupted Images

Abstract

References

Related Articles

Cited by

Periodical cited type(1)

Other cited types(3)

Catalog

Related

A Novel Median Filter to Iteratively Remove Salt-and-Pepper Noise from Highly Corrupted Images

Abstract

References

Related Articles

Cited by

Periodical cited type(1)

Other cited types(3)

Catalog

Related

Export File

Citation

Format

Content