Image Denoising Thesis

Image Denoising Thesis-7
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.The supplemental files are provided "AS IS" without warranty.The application of bilateral filter on the approximation subband results in loss of some image details, whereas that after each level of wavelet reconstruction flattens the gray levels thereby resulting in a cartoon-like appearance.

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.The supplemental files are provided "AS IS" without warranty.

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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.

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.In Gaussian noise scenarios, the performance of proposed methods is compared with existing denoising methods and found that, it has inferior performance compared to Bayesian least squares estimate using Gaussian Scale mixture and superior/comparable performance to that of wavelet thresholding, bilateral filter, multi-resolution bilateral filter, NL-means and Kernel based methods.Further, proposed methods have the advantage of less computational time compared to other methods except wavelet thresholding, bilateral filter.(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.Pro Quest is not responsible for the content, format or impact on the supplemental file(s) on our system.in some cases, the file type may be unknown or may be a file. Copyright of the original materials contained in the supplemental file is retained by the author and your access to the supplemental files is subject to the Pro Quest Terms and Conditions of use.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.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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