Total views : 362

Image Denoising using Various Wavelet Transforms: A Survey


  • Department of EECE, The NorthCap University, Gurgaon - 122017, Haryana, India


Objectives:Image processing basically comprises of techniques employed to either enhance or restore an image. Noise may creep into the image anywhere from acquisition to transmission phase.Denoising of images can be done in spatial or frequency domain.In this paper we have compared the work done by different researchers in the domain of image restoration using wavelets. Methods/Statistical Analysis: wavelet transform has proven to be an efficient and effective method to remove noise. Researchers have explored various types of wavelets and their variations and combinations for image denoising and restoration.Performance is measured in terms of PSNR, MSE and visual quality. Many of the current techniques assume the noise model to be Gaussian. Findings:On studying work of various researchers we got to know that as level of decomposition increases performance of denoising technique improves, third and fourth level of decomposition has good results. Wavelet transform performs better than normal average filtering, gaussian filtering and wiener filters. Intra scale and interscale correlations of non orthogonal wavelet coefficients need to utilized by developing good statistical models.And thresholding process needs to be optimized that is value of threshold has to be computed with strong statistical models. Application/Improvements: As we know image processing finds application in all most all spheres of life like medical science, remote sensing, military, space exploration etc.,


Decomposition, Image Denoising, Restoration, Threshold, Wavelets.

Full Text:

 |  (PDF views: 451)


  • Abhijit D, Gaikwad, Sugumaran V, Amarnath M. Fault Diagnosis of Roller Bearings with Sound Signals using Wavelets and Decision Tree Algorithm. Indian Journal of Science and Technology. 2016 Sep,;9(33):1–7.
  • Abhijit VD, Sugumaran V, Ramachandran KI. Fault Diagnosis of Bearings using Vibration Signals and Wavelets.Indian Journal of Science and Technology. 2016 Sep; 9(33):1–7.
  • Memon AP, Uqaili MA, Memon ZA, Ali AA, Zafar A, “Combined Novel Approach of DWT and Feedforward MLP-RBF Network for the Classification of Power Signal Waveform Distortion”. Indian Journal of Science and Technology. 2014 May; 7(5): 710–22.
  • Modi TM, Anilkumar PH, Alex JSR. Low Complexity DWT Architecture Implementation for Feature Extraction using Different Multipliers. Indian Journal of Science and Technology. 2015 Sep; 8(21):1–7.
  • Sathasivam S, Rahamathulla SK ,Implementation of HDB3 Encoder Chip Design. Indian Journal of Science and Technology. Feb 2016; 9(5): 1–7.
  • Jhingan A, Kansal L,Performance Analysis of FFT-OFDM and DWT-OFDMover AWGN Channel under the Effect of CFO. Indian Journal of Science and Technology. 2016 Feb; 9(6):1–7.
  • Kekre H B, Sarode T, Vig R.Multi-resolution Analysis of Multi-spectral Palmprints using Hybrid Wavelets for Identification. International Journal of Advanced Computer Science and Applications (IJACSA).March 2013;4(3):192–98.
  • Monika , Chaudhary P , Lalit G, Lifting Scheme Using HAAR and Biorthogonal Wavelets For Image Compression. International Journal of Engineering Research and Applications (IJERA). Jul-Aug 2013; 3(4):474–78.
  • Kekre H B, Sarode T K, Vig R. A new multi-resolution hybrid wavelet for analysis and image compression. International Journal Of Electronics. July 2015; 102(12): 2108–26.
  • Singh SK, Singh KB, Singh VI. Medical Image De noising In The Wavelet Domain Using Haar And Db3 Filtering. International Refereed Journal of Engineering and Science (IRJES). September 2012; 1(1):01–8.
  • Jumah A. Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques. Journal of Signal and Information Processing. Feb 2013; 4(1):33–41.
  • Om H, Biswas M. An Improved Image Denoising Method Based on Wavelet Thresholding. Journal of Signal and Information Processing. Sept 2012; 3(1):109–16.
  • Kaur G, Kaur R. Image De-Noising using Wavelet Transform and various Filters. International Journal of Research in Computer Science. March 2012;2(2):15–21.
  • Ruikar SD, Doye DD. Wavelet Based Image Denoising Technique. International Journal of Advanced Computer Science and Applications. March 2011; 2(3):49–53.
  • Sudha S, Suresh GR, Sukanesh R. Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding based on Weighted Variance‖. International Journal of Computer Theory and Engineering. April 2009;1(1):7–12.
  • Achim A, Bezerianos A, Tsakalides P. Wavelet-based ultrasound image denoising using an alpha-stable prior probability model , Proceedings of 8th IEEE International Conference on Image Processing Greece: 2001. 2.p.221–4.
  • Kaur L, Gupta S, Chauhan R C, Image Denoising using Wavelet Thresholding. 3rd Conference on Computer Vision, Graphics and Image Processing. India:2002. p. 16–8.
  • Mohideen SK, Perumal SA, Sathik MM, Image De-noising using Discrete Wavelet transform. International Journal of Computer Science and Network Security (IJCSNS). January 2008;8(1):213–6.
  • Ramani S, Blu T, Unser M, Monte-Carlo SURE: A blackbox optimization of regularization parameters for general denoising algorithms, IEEE Transactions on Image Processing. Sept 2008;17(9):1540–54.
  • Wilson, Roland, Rajpoot, Nasir M. Image volume denoising using a Fourier-wavelet basis. 6th Baiona Workshop on Signal Processing in Communications , Baiona, Spain, Sep 2003.
  • Mohideen K , Perumal A, Krishnan, Sathik M .Image Denoising And Enhancement Using Multiwavelet With Hard Threshold In Digital Mammographic Images. International Arab Journal of e-Technology.Jan 2011; 2(1):49–55.
  • Rakheja P, Vig R. Image Denoising using Combination of Median Filtering and Wavelet Transform. International Journal of Computer Applications. May 2016; 141(9):31–5.
  • Satheesh S, Prasad K V S V R. Medical image denoising using adaptive threshold based on contourlet transform. Advanced Computing: An International Journal ( ACIJ ). March 2011; 2(2): 52–8.
  • Chang S, Grace, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE transactions on image processing. Sept 2000; 9 (9): 1532–46.
  • Veena P V, Devi G R, Sowmya V, Soman K P,Least Square based Image Denoising using Wavelet Filters. Indian Journal of Science and Technology. Aug 2016; 9(30):1–6.
  • Deepa K, Dixon M, Ajay A, Sowmya V, Soman K P,Aerial and Satellite Image Denoising using Least Square Weighted Regularization Method. Indian Journal of Science and Technology. Aug 2016; 9(30):1–10.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.