Title page for ETD etd-1219102-152426


Type of Document Master's Thesis
Author Dangeti, Sarita Veera
Author's Email Address sdange1@lsu.edu
URN etd-1219102-152426
Title Denoising Techniques - A Comparison
Degree Master of Science in Electrical Engineering (M.S.E.E.)
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Suresh Rai Committee Chair
Jerry Trahan Committee Member
Subash Kak Committee Member
Keywords
  • visushrink
  • wavelets
  • median filter
  • lms adaptive filter
  • mean filter
  • image denoising
  • sureshrink
  • bayesshrink
  • multifractal denoising
Date of Defense 2002-12-11
Availability unrestricted
Abstract
Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms, such as filtering approach, wavelet based approach, and multifractal approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Brownian noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise. In the case where the noise characteristics are complex, the multifractal approach can be used. A quantitative measure of comparison is provided by the signal to noise ratio of the image.
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