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In-depth Analysis of Wavelet Transform based Denoising Scheme for Smooth and Textured Images Corrupted with Gaussian Noise

Gopal Prasad, Arun Kumar Mishra, Atul Kumar Singh Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
10.5120/ijais14-451083
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  1. Gopal Prasad, Arun Kumar Mishra and Atul Kumar Singh. Article: In-depth Analysis of Wavelet Transform based Denoising Scheme for Smooth and Textured Images Corrupted with Gaussian Noise. International Journal of Applied Information Systems 6(7):27-32, January 2014. BibTeX

    @article{key:article,
    	author = "Gopal Prasad and Arun Kumar Mishra and Atul Kumar Singh",
    	title = "Article: In-depth Analysis of Wavelet Transform based Denoising Scheme for Smooth and Textured Images Corrupted with Gaussian Noise",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 6,
    	number = 7,
    	pages = "27-32",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Image denoising involves the manipulation of the image data to produce a clear and high quality image. Selection of the denoising algorithm is depends on the types of images and applications area of images. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. Wavelet based approach is Nobel approach for denoising smooth and textured images corrupted with Gaussian noise. This paper proposed the wavelet based approach with level depending threshold calculated by modified 'sqtwolog' method (universal method) at each scale on the images corrupted with Gaussian noise and performs their in-depth study by considering five major wavelet families like Haar, Daubecheis, Coiflets, Symlets and Biorthogonals. The noisy wavelet coefficients are threshold by Soft Threshold method. The edge preservation and sparse representation abilities of wavelet transform is utilized. A quantitative measure of comparison between original image and denoised image is provided by the PSNR (peak signal to noise ratio) for the smooth and textured images.

Reference

  1. D. L. Donoho, "De-noising by soft-thresholding", IEEE Trans. Information Theory, vol. 41, no. 3, pp. 613- 627, May1995. http://wwwstat. stanford. edu/~donoho/Reports/1992/denoisereleas e3. ps. Z.
  2. S. G. Mallat and W. L. Hwang, "Singularity detection and processing with wavelets," IEEE Trans. Inform. Theory, vol. 38, pp. 617–643, Mar. 1992.
  3. David L. Donoho and Iain M. Johnstone, "Adapting to Unknown Smoothness via Wavelet Shrinkage," Journal of American StatisticalAssociation, 90(432):1200-1224, December 1995.
  4. R. Coifman and D. Donoho, "Translation invariant de-noising," in Lecture Notes in Statistics: Wavelets and Statistics, vol. New York: Springer-Verlag, pp. 125-150, 1995.
  5. Amara Graps, "An Introduction to Wavelets," IEEE Computational Science and Engineering, summer 1995, Vol 2, No. 2.
  6. Matlab 7. 8, "Wavelet tool box,".
  7. Matlab7. 8, "Matlab," http://www. mathworks. com/, May 2009.
  8. D. L. Donoho, "De-noising by soft thresholding", IEEE Transaction on Information Theory, Vol. 41, pp. 613-627, 1995.
  9. Image Processing Fundamentals-Statistics, "Signal to Noise ratio," http://www. ph. tn. tudelft. nl/courses/FIP/no frames/fip-Statisti . html, 2001.

Keywords

Discrete Wavelet Transform (DWT), Wavelet Thresholding, Denoising, Smooth and Textured Images, PSNR (Peak Signal to Noise Ratio).