Image Denoising Thesis

The noise considered in this thesis is additive white Gaussian noise (AWGN).Some spatial-Domain filters like Mean filter, Median filter, Weighted median filter, Wiener filter etc.The Gaussian filter is a local and linear filter that smoothes the whole image irrespective of its edges or details, whereas the bilateral filter is also a local but non-linear, considers both gray level similarities and geometric closeness of the neighboring pixels without smoothing edges.

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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.(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 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.To tackle these issues, it is proposed to use the blend of Gaussian/bilateral filter and its method noise thresholding using wavelets.We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services.To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy.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 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.