• DocumentCode
    2312384
  • Title

    Total Variation Based Wavelet Domain Filter for Image Denoising

  • Author

    Bhoi, Nilamani ; Meher, Sukadev

  • Author_Institution
    Dept. of Electron. & Commun. Eng., Nat. Inst. of Technol., Rourkela
  • fYear
    2008
  • fDate
    16-18 July 2008
  • Firstpage
    20
  • Lastpage
    25
  • Abstract
    In this paper the method total variation (TV) is applied on noisy image decomposed in wavelet domain for removal of additive white gaussian noise (AWGN). LL subband of a single decomposed noisy image is used to find the horizontal, vertical and diagonal edges. Using the pixel position of horizontal edges, the corresponding wavelet coefficients in HL subband is retained thresholding others to zero. Adopting the same procedure the vertical and diagonal details of LH and HH subband is retained. The method TV is applied to LL subband for one iteration only. Applying inverse wavelet transform on modified wavelet coefficients we get back the image with little noise. This little noise can be removed using TV filter with single iteration. The method performs well in terms of peak signal to noise ratio (PSNR) over many well known spatial and wavelet domain methods. The method also retains the edges and other detailed information very well.
  • Keywords
    AWGN; edge detection; filtering theory; image denoising; wavelet transforms; HL subband; additive white gaussian noise; image denoising; inverse wavelet transform; peak signal to noise ratio; single decomposed noisy image; wavelet coefficients; wavelet domain filter; AWGN; Additive white noise; Filters; Gaussian noise; Image denoising; PSNR; TV; Wavelet coefficients; Wavelet domain; Wavelet transforms; Discrete Wavelet Transform; Gaussian noise; Peak signal to noise ratio; Total variation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on
  • Conference_Location
    Nagpur, Maharashtra
  • Print_ISBN
    978-0-7695-3267-7
  • Electronic_ISBN
    978-0-7695-3267-7
  • Type

    conf

  • DOI
    10.1109/ICETET.2008.6
  • Filename
    4579859