An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

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Mr. N.Naveen Kumar
Mr. N.Naveen Kumar
σ
Mr. N. Naveen Kumar
Mr. N. Naveen Kumar
ρ
Dr.S.Ramakrishna
Dr.S.Ramakrishna
α Sri Venkateswara University Sri Venkateswara University

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An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

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Abstract

Impulse noise in images is present because of bit errors in transmission or introduced during the signal acquisition stage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In our proposed method, first we apply the Stationary wavelet transform for noise added image. It will separate into four bands like LL, LH, HL and HH. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0’s and 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard median filter (MF), decision based algorithm (DBA). The 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
  1. V Jayaraj,D Ebenezer (2010). A New and Efficient Algorithm for the Removal of High Density Salt and Pepper Noise in Images and Videos.
  2. Yi Wan,Qiqiang Chen (2010). A novel quadratic type variational method for efficient salt-and-pepper noise removal.
  3. Wang Jian,Qin Wu,Dong Xiao-Gang (2011). A new adaptive weight algorithm for salt and pepper noise removal.
  4. Jia-Shiuan Tsai,Ching-Te Chiu Switching Bilateral Filter with a Texture/Noise Detector for Universal Noise Removal.
  5. S Esakkirajan,I Vennila (2011). Salt and pepper noise removal in video using adaptive decision based median filter.
  6. S Sheebha,L Sriraman (2010). A Modified Algorithm for Removal of Salt and Pepper Noise in Color Images.
  7. M Rahman,S Ashfaqueuddin (2011). An enhanced decision based adaptive median filtering technique to remove Salt and Pepper noise in digital images.
  8. H Rabbani (2011). Wavelet-based medical infrared image noise reduction using local model for signal and noise.
  9. S Sarath,P Palanisamy (2011). Detection and removal of Salt and Pepper noise in images by improved median filter.
  10. Yi-Ta Wu,Yih-Tyng Wu,Shau-Yin Tseng,Chao-Yi Cho (2011). An Impulse Noise Removal Algorithm by Considering Region-Wise Property for Color Image.

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. 2012. \u201cAn Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 12 (GJCST Volume 12 Issue F12): .

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Issue Cover
GJCST Volume 12 Issue F12
Pg. 35- 40
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Version of record

v1.2

Issue date

August 20, 2012

Language
en
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Published Article

Impulse noise in images is present because of bit errors in transmission or introduced during the signal acquisition stage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In our proposed method, first we apply the Stationary wavelet transform for noise added image. It will separate into four bands like LL, LH, HL and HH. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0’s and 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard median filter (MF), decision based algorithm (DBA). The 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.

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An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

Mr. N. Naveen Kumar
Mr. N. Naveen Kumar
Dr.S.Ramakrishna
Dr.S.Ramakrishna

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