Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/310187
Title: Investigation of multiresolution image denoising schemes using wavelet transforms
Researcher: Laavanya M
Guide(s): Karthikeyan M
Keywords: Engineering and Technology
Engineering
Engineering Electrical and Electronic
wavelet transforms
multiresolution
University: Anna University
Completed Date: 2019
Abstract: Image denoising is a fundamental problem in image processing. The main determination is to quash noise from the degraded image while keeping other details of the image unchanged. In recent years, many multiresolution based approaches have attained great success in image denoising In a nut shell, the wavelet transform provide an optimal representation of a noisy image, with information bearing signal from a small number of coefficients and noise by all other remaining coefficients. The noisy coefficients can be eliminated by thresholding procedure. Thus, every method of image denoising using wavelets has three basic steps: to compute the wavelet transforms of the noisy image, threshold the wavelet coefficients and finally to compute the inverse wavelet transform. The effectiveness of the noisy coefficient separation of the image depends on the capability of sparse representation of the image. The wavelet representation is optimally sparse, since the wavelets overlapping a singularity have a large wavelet coefficient and all other coefficients are small. Utmost the noisy wavelet coefficient shrinkage is better, only if the threshold value is properly selected. The denoised images obtained using Discrete Wavelet Transform (DWT) suffers from shift invariance and poor directional selectivity. These drawbacks can be overcome by the use of 2D-Dual Tree Discrete Wavelet Transform (DTDWT). The 2D-DTDWT converts an N-point signal into M coefficients with . newline
Pagination: xxiii, 168p.
URI: http://hdl.handle.net/10603/310187
Appears in Departments:Faculty of Information and Communication Engineering

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07_chapter1.pdf944.34 kBAdobe PDFView/Open
08_chapter2.pdf2.04 MBAdobe PDFView/Open
09_chapter3.pdf1.21 MBAdobe PDFView/Open
10_chapter4.pdf1.12 MBAdobe PDFView/Open
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12_chapter6.pdf1.12 MBAdobe PDFView/Open
13_chapter7.pdf1.22 MBAdobe PDFView/Open
14_chapter8.pdf1.48 MBAdobe PDFView/Open
15_conclusion.pdf139.34 kBAdobe PDFView/Open
16_references.pdf165.29 kBAdobe PDFView/Open
17_listofpublications.pdf133.72 kBAdobe PDFView/Open
80_recommendation.pdf175.06 kBAdobe PDFView/Open


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