No Access

A Statistical Salt-and-Pepper Noise Removal Algorithm

St. Thomas’ College of Engineering and Technology, Computer Science and Engineering Department, 4-D. H. Road, Kolkata 23, West Bengal, India

E-mail Address: amiya.halder77@gmail.com

Corresponding author.

Search for more papers by this author

Sayan Halder

St. Thomas’ College of Engineering and Technology, Computer Science and Engineering Department, 4-D. H. Road, Kolkata 23, West Bengal, India

E-mail Address: sayanhaldervms@gmail.com

Search for more papers by this author

Samrat Chakraborty

St. Thomas’ College of Engineering and Technology, Computer Science and Engineering Department, 4-D. H. Road, Kolkata 23, West Bengal, India

E-mail Address: i.am.samrat75@gmail.com

Search for more papers by this author

, and

Apurba Sarkar

Indian Institute of Engineering Science and Technology, Computer Science and Technology Department, Howrah 711103, West Bengal, India

E-mail Address: as.besu@gmail.com

Search for more papers by this author

https://doi.org/10.1142/S0219467819500062Cited by:1 (Source: Crossref)

Abstract

This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99% and is found to perform better.

Keywords:

Remember to check out the Check out our Most Cited Articles!
Check out these titles on Image Analysis

References

1. R. C. Gonzalez and R. E. Woods , Digital Image Processing, 2nd edn. (Prentice Hall, New York, 2002). Google Scholar
2. M. G. L. Yin, R. Yang and Y. Neuvo , “Weighted median filters: A tutorial,” IEEE Trans. Circuits Syst. II 43 (3), 157–192 (1996). Google Scholar
3. S. J. Ko and Y. H. Lee , “Center weighted median filters and their applications to image enhancement,” IEEE Trans. Circuits Syst. 38 (9), 984–993 (1991). Google Scholar
4. T. Sun and Y. Neuvo , “Detail-preserving median based filters in image processing,” Pattern Recognit. Lett. 15, 341–347 (1994). Web of Science, Google Scholar
5. G. Qiu , “An improved recursive median filtering scheme for image processing,” IEEE Transactions on Image Processing 5 (4), 646–648 (1996). Web of Science, Google Scholar
6. Z. Wang and D. Zhang , “Progressive switching median filter for the removal of impulse noise from highly corrupted images, IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process 46 (1), 78–80 (1999). Google Scholar
7. W. Luo , “An efficient detail-preserving approach for removing impulse noise in images,” IEEE Signal Process. Lett. 13 (7), 413–416 (2006). Web of Science, Google Scholar
8. K. K. V. Toh, H. Ibrahim and M. N. Mahyuddin , “Salt-and-pepper noise detection and reduction using fuzzy switching median filter,” IEEE Trans. Consum. Electron. 54 (4), 1956–1961 (2008). Web of Science, Google Scholar
9. K. K. V. Toh and N. A. M. Isa , “Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction,” IEEE Signal Process. Lett. 17 (3), 281–284 (2010). Web of Science, Google Scholar
10. K. S. Srinivasan and D. Ebenezer , “A new fast and efficient decision-based algorithm for removal of high-density impulse noises,” IEEE Signal Process. Lett. 14 (3), 189–192 (2007). Web of Science, Google Scholar
11. 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 Signal Process. Lett. 18 (5), 287–290 (2011). Web of Science, Google Scholar
12. M. S. Nair and G. Raju , “A new fuzzy-based decision algorithm for high-density impulse noise removal,” SIViP 6, 579–595 (2012). Google Scholar
13. P. Zhang and F. Li , “A new adaptive weighted mean filter for removing salt-and-pepper noise,” IEEE Signal Process. Lett. 21 (10), 1280–1283 (2014). Web of Science, Google Scholar
14. P. Karthikeyan and S. Vasuki , “Hybrid approach of efficient decision-based algorithm and fuzzy logic for the removal of high density salt-and-pepper noise in images,” J. Circuit. Syst. Comput. 25 (10), 16501301 (1–20) (2016). Link, Web of Science, Google Scholar
15. Y. Wang, J. Wang, X. Song and L. Han , “An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal,” IEEE Signal Process. Lett. 23 (11), 1582–1586 (2016). Web of Science, Google Scholar
16. V. Gupta, V. Chaurasia and Madhu Shandilya , “Random-valued impulse noise removal using adaptive dual threshold median filter,” J. Vis. Commun. Image R. 26, 296–304 (2016). Web of Science, Google Scholar
17. M. H. Hsieh, F. C. Cheng, M. C. Shie and S. J. Ruan , “Fast and efficient median filter for removing high levels of salt-and-pepper noise images,” J. Vis. Commun. Image R. 26, 1333–1338 (2016). Google Scholar
18. X. Wanga, S. Shen, G. Shi, Y. Xu and P. Zhang , “Iterative non-local means filter for salt-and-pepper noise removal,” J. Vis. Commun. Image R. 38, 440–450 (2016). Web of Science, Google Scholar
19. Q. Q. Chen, M. H. Hung and F. Zou , “Effective and adaptive algorithm for pepper-and-salt noise removal,” IET Image Process. 11 (9), 709–716 (2017). Web of Science, Google Scholar
20. R. Varatharajan, K. Vasanth, M. Gunasekaran, M. Priyan and X. Gao , “An adaptive decision based kriging interpolation algorithm for the removal of high density salt-and-pepper noise in images,” Int. J. Comput. Electr. Eng. 2, 709–716 (2017). Google Scholar
21. U. Erkan, L. Gokrem and S. Enginoglu , “Different applied median filter in salt-and-pepper noise,” Int. J. Comput. Electr. Eng. 70, 1–10 (2018). Google Scholar
22. X. Liu, X. Y. Jing, G. Tang, F. Wu and Q. Ge , “Image denoising using weighted nuclear norm minimization with multiple strategies,” Signal Process. 135 (C), 239–252 (2017). Web of Science, Google Scholar