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Comparative Analysis of Gaussian Filter with Wavelet Denoising for Various Noises Present in Images


  • I.K.G. P.T.U., Jalandhar – 144603, Punjab, India
  • Department of Electronics and Communication Engineering, DAVIET, Jalandhar - 144008, Punjab, India


Objectives: This paper is providing a comparative performance analysis of wavelet denoising with Gaussian filter applied on images contaminated with various noises. Gaussian filter is a basic filter used in image processing. Its response is varying with its kernel sizes that have also been shown in analysis. Wavelet based de-noising is also one of the way of removing various noises usually present in images. Wavelet transform is used to convert the images to wavelet domain. Based on thresholding operations in wavelet domain noise could be removed from images. Methods/Analysis: In this paper, image quality matrices like PSNR and MSE have been compared for the various types of noises in images for different denoising methods. Moreover, the behavior of different methods for image denoising have been graphically shown in paper with MATLAB based simulations. Findings: In the end wavelet based de-noising methods has been compared with Gaussian based filter. The paper provides a review of filters and their denoising analysis under different noise conditions.


Denoising, Gaussian Filter, MSE, PSNR, SNR, Thresholding, Wavelet Transform.

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  • Gonzale RC, Woods RE. Digital image processing. Second edition, Upper Saddle River, NJ: Prentice Hall; 2006.
  • Haralick R, Shapiro L. Computer and robot vision. Chapter 7. Addison-Wesley Publishing Company; 1992.
  • Vernon D. Machine vision. Prentice-Hall. 1991; p.59–61,214.
  • Gonzale RC, Woods RE. Digital image processing using Matlab. Third edition, Pearson Education; 2005.
  • Pratt WK. Digital image processing. Third edition, A Wiley Inter Science Publication; 2001.
  • Zhou H, Wu J, Zhang J. Digital image processing. Part II.Bookboon; 2010.
  • Jain AK. Fundamentals of digital image processing. Prentice Hall of India; 2002.
  • Sridhar S. Digital image processing. Oxford University Press; 2011.
  • Jähne B. Digital image processing. Sixth edition, Springer; 2005.
  • Zhou H, Wu J, Zhang J. Digital image processing. Part I, Bookboon; 2010.
  • Chan RH, Ho C-W, Nikolova M. Salt -and-pepper noise removal by median -type noise detectors and detail preserving regularization. IEEE Transactions on Image Processing.2005 Oct; 14(10).
  • Hsiao P-Y, Chou S-S, Huang F-C. Generic 2-D Gaussian smoothing filter for noisy image processing. TENCON 2007 – 2007 IEEE Region 10 Conference; 2007. p. 1–4.
  • Barcelos CAZ, Batista MA. Image restoration using digital inpainting and noise removal. Image and Vision Computing. 2007; 25:61–9.
  • Liu C, Szeliski R, Kang SB, Zitnick CL, Freeman WT.Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2008 Feb; 30(2).
  • Mohideen SK, Perumal DA, Sathik MM. Image de-noising using discrete wavelet transform. International Journal of Computer Science and Network Security. 2008 Jan; 8(1).
  • Anutam, Rajni. Performance analysis of image denoising with wavelet thresholding methods for different levels of decomposition. The International Journal of Multimedia and Its Applications. 2014 Jun; 6(3).
  • Ram I, EladM. Generalized tree-based wavelet transform.IEEE Transactions on Signal Processing. 2011 Sep; 59(9).
  • Rupinderpal. Survey of de-noising methods using filters and fast wavelet transform. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Feb; 3(2):133–6.
  • Mohideen SK, Perumal DA, Sathik MM. Image de-noising using discrete wavelet transform. International Journal of Computer Science and Network Security. 2008 Jan; 8(1).
  • Fathi A, Naghsh-Nilchi AR. Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transaction on Image Processing; 2012 Sep; 21(9).
  • Gagnon, L. Wavelet filtering of speckle noise- ome numerical results. Proceedings of the Conference Vision Interface, Trois-Reveres; 1999.
  • Donoho DL. De-noising by soft-thresholding. IEEE Transactions on Information Theory. 1995 May; 41:613–27.
  • Chang SG, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing. 2000 Sep; 9(9).


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