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Nonlocal and Randomized Methods in S...

Crandall, Robert.

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Nonlocal and Randomized Methods in Sparse Signal and Image Processing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlocal and Randomized Methods in Sparse Signal and Image Processing./
Author:	Crandall, Robert.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	96 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
Contained By:	Dissertations Abstracts International80-02B.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10840330
ISBN:	9780438205963

Nonlocal and Randomized Methods in Sparse Signal and Image Processing.
Crandall, Robert.

Nonlocal and Randomized Methods in Sparse Signal and Image Processing. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 96 p.

Source: Dissertations Abstracts International, Volume: 80-02, Section: B.

Thesis (Ph.D.)--The University of Arizona, 2018.

This item must not be sold to any third party vendors.

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. (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.

ISBN: 9780438205963Subjects--Topical Terms:

1669109
Applied Mathematics.

Nonlocal and Randomized Methods in Sparse Signal and Image Processing.
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100 1 $a Crandall, Robert. $3 3437604
245 1 0 $a Nonlocal and Randomized Methods in Sparse Signal and Image Processing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 96 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Bilgin, Ali.
502 $a Thesis (Ph.D.)--The University of Arizona, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a 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. (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.
590 $a School code: 0009.
650 4 $a Applied Mathematics. $3 1669109
690 $a 0364
710 2 $a The University of Arizona. $b Applied Mathematics. $3 1266624
773 0 $t Dissertations Abstracts International $g 80-02B.
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791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10840330