Image Denoising Thesis

Image Denoising Thesis-41
This noise gets present amid acquisition, transmission, and storage processes.

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This fractal-based denoising algorithm can be applied in the pixel and the wavelet domains of the noisy image using standard fractal and fractal-wavelet schemes, respectively.

Furthermore, the cycle spinning idea was implemented in order to enhance the quality of the fractally denoised estimates.

In particular, three new image denoising methods are proposed: context-based wavelet thresholding, predictive fractal image denoising and fractal-wavelet image denoising.

The proposed context-based thresholding strategy adopts localized hard and soft thresholding operators which take in consideration the content of an immediate neighborhood of a wavelet coefficient before thresholding it.

The need for image enhancement and restoration is encountered in many practical applications.

For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. After reviewing standard image denoising methods as applied in the spatial, frequency and wavelet domains of the noisy image, the thesis embarks on the endeavor of developing and experimenting with new image denoising methods based on fractal and wavelet transforms.Depending on the size of the file(s) you are downloading, the system may take some time to download them. We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising.The two fractal-based predictive schemes are based on a simple yet effective algorithm for estimating the fractal code of the original noise-free image from the noisy one.From this predicted code, one can then reconstruct a fractally denoised estimate of the original image.This thesis focuses on the topics of sparse and non-local signal and image processing.In particular, I present novel algorithms that exploit a combination of sparse and non-local data models to perform tasks such as compressed-sensing reconstruction, image compression, and image denoising.The contributions in this thesis are: (1) a fast, approximate minimum mean-squared error (MMSE) estimation algorithm for sparse signal reconstruction, called Randomized Iterative Hard Thresholding (RIHT).This algorithm has applications in compressed sensing, image denoising, and other sparse inverse problems.(2) An extension to the Block-Matching 3D (BM3D) denoising algorithm that matches blocks at different rotation angles.This algorithm improves on the performance of BM3D in terms of both visual quality and quantitative denoising accuracy.


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