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New Image Denoising Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	New Image Denoising Algorithms./
Author:	Aghajarian, Mickael.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	96 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28548887
ISBN:	9798538117000

New Image Denoising Algorithms.
Aghajarian, Mickael.

New Image Denoising Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 96 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--University of Wyoming, 2021.

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

Image noise is unwanted fluctuations in pixel intensities that is often inevitable during the process of acquisition, compression, and transmission for many reasons such as imperfections in capturing instruments, limitations of analog-to-digital converters, and interference in transmission channels. The existence of noise not only degrades the visual quality of images but also adversely affects the performance of image processing and computer vision tasks, such as classification, detection, and segmentation. Thus, removing or attenuating the effect of image noise (i.e., image denoising) is often an essential preprocessing task in the field of image processing and computer vision.In this dissertation, we addressed three image noises: salt-and-pepper (SAP) impulse noise, random-valued impulse noise (RVIN), and Gaussian noise. In order to restore images contaminated by SAP impulse noise, we used the modified mean filter and total variation of corrupted pixels that was minimized by using convex optimization. For RVIN, we implemented a three-step method that restored images by estimating the noise density, detecting the noisy pixels, and applying a modified weighted mean filter to the detected noisy pixels. For images corrupted by Gaussian noise, we proposed a deep convolutional neural network that handled a wide range of noise levels by using two trained models, one for low noise and the other for high noise levels. We compared the performance of our noise removal methods with other state-of-the-art algorithms and in the vast majority of the cases, our method outperformed other image denoising algorithms which showed the effectiveness of the proposed methods.

ISBN: 9798538117000Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Deep Learning

New Image Denoising Algorithms.
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100 1 $a Aghajarian, Mickael. $3 3688983
245 1 0 $a New Image Denoising Algorithms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 96 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: McInroy, John E.
502 $a Thesis (Ph.D.)--University of Wyoming, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Image noise is unwanted fluctuations in pixel intensities that is often inevitable during the process of acquisition, compression, and transmission for many reasons such as imperfections in capturing instruments, limitations of analog-to-digital converters, and interference in transmission channels. The existence of noise not only degrades the visual quality of images but also adversely affects the performance of image processing and computer vision tasks, such as classification, detection, and segmentation. Thus, removing or attenuating the effect of image noise (i.e., image denoising) is often an essential preprocessing task in the field of image processing and computer vision.In this dissertation, we addressed three image noises: salt-and-pepper (SAP) impulse noise, random-valued impulse noise (RVIN), and Gaussian noise. In order to restore images contaminated by SAP impulse noise, we used the modified mean filter and total variation of corrupted pixels that was minimized by using convex optimization. For RVIN, we implemented a three-step method that restored images by estimating the noise density, detecting the noisy pixels, and applying a modified weighted mean filter to the detected noisy pixels. For images corrupted by Gaussian noise, we proposed a deep convolutional neural network that handled a wide range of noise levels by using two trained models, one for low noise and the other for high noise levels. We compared the performance of our noise removal methods with other state-of-the-art algorithms and in the vast majority of the cases, our method outperformed other image denoising algorithms which showed the effectiveness of the proposed methods.
590 $a School code: 0264.
650 4 $a Electrical engineering. $3 649834
650 4 $a Statistics. $3 517247
650 4 $a Statistical physics. $3 536281
650 4 $a Artificial intelligence. $3 516317
650 4 $a Information science. $3 554358
653 $a Deep Learning
653 $a Gaussian Noise
653 $a Image Denoising
653 $a Random-valued Impulse Noise
653 $a Salt-and-pepper Impulse Noise
690 $a 0544
690 $a 0463
690 $a 0723
690 $a 0800
690 $a 0217
710 2 $a University of Wyoming. $b Electrical & Computer Engineering. $3 1018769
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0264
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28548887