An Efficient Approach of Removing the High Density Salt & Pepper Noise Using Stationary Wavelet Transform

Mr. N.Naveen Kumar

An Efficient Approach of Removing the High Density Salt & Pepper Noise Using Stationary Wavelet Transform

Mr. N.Naveen Kumar * §

Contact

§ Sri Venkateswara University, Tirupati

An Efficient Approach of Removing the High Density Salt & Pepper Noise Using Stationary Wavelet Transform

Article Fingerprint

ReserarchID

CSTIU3O5

An Efficient Approach of Removing the High Density Salt & Pepper Noise Using Stationary Wavelet Transform Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

This converted HTML version may contain rendering inconsistencies. Please refer to the PDF for the authoritative version, or click here to provide feedback.

Abstract

Images are often corrupted by impulse noise, also known as salt and pepper noise. Salt and pepper noise can corrupt the images where the corrupted pixel takes either maximum or minimum gray level. Amongst these standard median filter has been established as reliable -method to remove the salt and pepper noise without harming the edge details. However, the major problem of standard Median Filter (MF) is that the filter is effective only at low noise densities. When the noise level is over 50% the edge details of the original image will not be preserved by standard median filter. Adaptive Median Filter (AMF) performs well at low noise densities. In our proposed method, first we apply the Stationary Wavelet Transform (SWT) for noise added image. It will separate into four bands like LL, LH, HL and HH. Further, we calculate the window size 3×3 for LL band image by Reading the pixels from the window, computing the minimum, maximum and median values from inside the window. Then we find out the noise and noise free pixels inside the window by applying our algorithm which replaces the noise pixels. The higher bands are smoothing by soft thresholding method. Then all the coefficients are decomposed by inverse stationary wavelet transform. The performance of the proposed algorithm is tested for various levels of noise corruption and compared with standard filters namely standard median filter (SMF), weighted median filter (WMF). Our proposed method performs well in removing low to medium density impulse noise with detail preservation up to a noise density of 70% and it gives better Peak Signal-to-Noise Ratio (PSNR) and Mean square error (MSE) values.

References

10 Cites in Article

Reference Format

1. H Hwang,R Haddad (1995). Adaptive median filters: new algorithms and results. IEEE Transactions on Image Processing, 4(4), 499-502.
2. Eduardo Abre,Michael Lightsone (1996). A New Efficient Approach for The Removal of Impulse Noise from Highly Corrupted Images. IEEE Transactions on Image Processing, 1012-1025.
3. Tao Chen,Kai-Kuang Ma,Li-Hui Chen (1999). Tri-State Median Filter for Image Denoising. IEEE Transactions on Image Processing, 1834-1838.
4. Zhou Wang,David Zhang (1999). Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images. IEEE Transactions on Circuits and Systems Analog And Digital Signal Processing, 78-80.
5. Tao Chen,Hong Wu (2001). Space Variant Median Filters for the Restoration of Impulse Noise Corrupted Images. IEEE Transactions on Circuits and Systems Analog and Digital Signal Processing, 784-789.
6. How-Lung Eng,Kai-Kuang Ma (2001). Noise Adaptive Soft-Switching Median Filter. IEEE Transactions on Image Processing, 242-251.
7. Shuqun Zhang,Mohmamad Karim (2002). A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises. IEEE Signal Letter, 360-363.
8. Wenbin Luo (2006). An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Processing Letters, 13(7), 413-416.
9. K Srinivasan,D Ebenezer (2007). A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises. IEEE Signal Processing Letters, 14(3), 189-192.
10. Dagao Duan,Quian Mo (2010). A Detail Preserving Filter for Impulse Noise removal. IEEE International Conference on Computer Application and System Modeling, 265-268.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Mr. N.Naveen Kumar. . "An Efficient Approach of Removing the High Density Salt & Pepper Noise Using Stationary Wavelet Transform". Global Journal of Computer Science and Technology GJCST Volume 12 (GJCST Volume 12 Issue 5).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 12 Issue 5

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST Classification G.1.2

Submission ReceivedApril 15, 2012
Peer Review Double Blind
Handling Editor
Accepted May 4, 2012
Published March 7, 2012

Version of record

v1.2

Issue date

March 7, 2012

Language

English

Order Article Reprint

Experience in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Mr. N.Naveen Kumar, Dr. S.Ramakrishna (PhD/Dr. count: 1)

View Count (all-time): 286

Total Views (Real + Logic): 4780

Total Downloads (simulated): 237

Publish Date: 2012 05, Tue

Monthly Totals (Real + Logic):

Month 1: 51 views
Month 2: 36 views
Month 3: 18 views
Month 4: 32 views
Month 5: 36 views
Month 6: 38 views
Month 7: 41 views
Month 8: 40 views
Month 9: 48 views
Month 10: 24 views
Month 11: 38 views
Month 12: 32 views
Month 13: 13 views
Month 14: 23 views
Month 15: 41 views
Month 16: 26 views
Month 17: 36 views
Month 18: 27 views
Month 19: 21 views
Month 20: 39 views
Month 21: 25 views
Month 22: 22 views
Month 23: 35 views
Month 24: 31 views
Month 25: 18 views
Month 26: 30 views
Month 27: 14 views
Month 28: 33 views
Month 29: 20 views
Month 30: 36 views
Month 31: 36 views
Month 32: 41 views
Month 33: 37 views
Month 34: 25 views
Month 35: 23 views
Month 36: 33 views
Month 37: 24 views
Month 38: 34 views
Month 39: 42 views
Month 40: 28 views
Month 41: 27 views
Month 42: 33 views
Month 43: 42 views
Month 44: 37 views
Month 45: 19 views
Month 46: 32 views
Month 47: 31 views
Month 48: 23 views
Month 49: 20 views
Month 50: 34 views
Month 51: 18 views
Month 52: 33 views
Month 53: 41 views
Month 54: 24 views
Month 55: 14 views
Month 56: 31 views
Month 57: 19 views
Month 58: 38 views
Month 59: 41 views
Month 60: 30 views
Month 61: 36 views
Month 62: 40 views
Month 63: 33 views
Month 64: 31 views
Month 65: 40 views
Month 66: 20 views
Month 67: 29 views
Month 68: 29 views
Month 69: 15 views
Month 70: 38 views
Month 71: 30 views
Month 72: 39 views
Month 73: 45 views
Month 74: 33 views
Month 75: 27 views
Month 76: 20 views
Month 77: 22 views
Month 78: 29 views
Month 79: 37 views
Month 80: 17 views
Month 81: 42 views
Month 82: 42 views
Month 83: 37 views
Month 84: 24 views
Month 85: 41 views
Month 86: 35 views
Month 87: 26 views
Month 88: 13 views
Month 89: 14 views
Month 90: 30 views
Month 91: 24 views
Month 92: 20 views
Month 93: 25 views
Month 94: 28 views
Month 95: 43 views
Month 96: 25 views
Month 97: 26 views
Month 98: 38 views
Month 99: 43 views
Month 100: 15 views
Month 101: 22 views
Month 102: 33 views
Month 103: 35 views
Month 104: 29 views
Month 105: 15 views
Month 106: 32 views
Month 107: 23 views
Month 108: 38 views
Month 109: 36 views
Month 110: 39 views
Month 111: 43 views
Month 112: 26 views
Month 113: 17 views
Month 114: 14 views
Month 115: 14 views
Month 116: 40 views
Month 117: 23 views
Month 118: 16 views
Month 119: 47 views
Month 120: 17 views
Month 121: 45 views
Month 122: 34 views
Month 123: 23 views
Month 124: 21 views
Month 125: 33 views
Month 126: 25 views
Month 127: 36 views
Month 128: 41 views
Month 129: 33 views
Month 130: 33 views
Month 131: 25 views
Month 132: 35 views
Month 133: 43 views
Month 134: 39 views
Month 135: 23 views
Month 136: 44 views
Month 137: 41 views
Month 138: 22 views
Month 139: 33 views
Month 140: 25 views
Month 141: 30 views
Month 142: 48 views
Month 143: 28 views
Month 144: 30 views
Month 145: 30 views
Month 146: 39 views
Month 147: 33 views
Month 148: 40 views
Month 149: 31 views
Month 150: 41 views
Month 151: 36 views
Month 152: 11 views
Month 153: 18 views
Month 154: 26 views
Month 155: 27 views
Month 156: 21 views
Month 157: 31 views
Month 158: 44 views
Month 159: 33 views
Month 160: 41 views
Month 161: 37 views
Month 162: 37 views
Month 163: 28 views
Month 164: 25 views
Month 165: 24 views
Month 166: 27 views
Month 167: 35 views
Month 168: 40 views
Month 169: 45 views
Month 170: 34 views
Month 171: 41 views
Month 172: 33 views
Month 173: 69 views
Month 174: 54 views

Total Views: 4.8K

Total Downloads: 237

All Trends