Image Denoising Thesis

Image Denoising Thesis-30
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.

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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.

The supplemental file or files you are about to download were provided to Pro Quest by the author as part of a dissertation or thesis.

Image Denoising Thesis

The supplemental files are provided "AS IS" without warranty.This noise gets present amid acquisition, transmission, and storage processes.Visual quality of the image is degraded due to the noise introduced in it.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.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.Image Denoising is an essential pre-processing task before the image is further processed by segmentation, feature extraction, texture analysis etc.Denoising is employed to evacuate the noise while retaining the sharp edges and other texture details of the image however much as could reasonably be expected.(3) A novel non-local, causal image prediction algorithm, and a corresponding codec implementation that achieves state of the art lossless compression performance on 8-bit grayscale images.(4) A deep convolutional neural network (CNN) architecture that achieves state-of-the-art results in bilnd image denoising, and a novel non-local deep network architecture that further improves performance.


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