東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Off-the-grid compressive imaging.

Ongie, Gregory J.

FindBook

Google Book

Amazon

博客來

Off-the-grid compressive imaging.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Off-the-grid compressive imaging./
作者:	Ongie, Gregory J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	134 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Contained By:	Dissertation Abstracts International78-05B(E).
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10181928
ISBN:	9781369304541

Off-the-grid compressive imaging.
Ongie, Gregory J.

Off-the-grid compressive imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 134 p.

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.

Thesis (Ph.D.)--The University of Iowa, 2016.

In many practical imaging scenarios, including computed tomography and magnetic resonance imaging (MRI), the goal is to reconstruct an image from few of its Fourier domain samples. Many state-of-the-art reconstruction techniques, such as total variation minimization, focus on discrete "on-the-grid" modeling of the problem both in spatial domain and Fourier domain. While such discrete-to-discrete models allow for fast algorithms, they can also result in sub-optimal sampling rates and reconstruction artifacts due to model mismatch. Instead, this thesis presents a framework for "off-the-grid", i.e. continuous domain, recovery of piecewise smooth signals from an optimal number of Fourier samples. The main idea is to model the edge set of the image as the level-set curve of a continuous domain band-limited function. Sampling guarantees can be derived for this framework by investigating the algebraic geometry of these curves. This model is put into a robust and efficient optimization framework by posing signal recovery entirely in Fourier domain as a structured low-rank (SLR) matrix completion problem. An efficient algorithm for this problem is derived, which is an order of magnitude faster than previous approaches for SLR matrix completion. This SLR approach based on off-the-grid modeling shows significant improvement over standard discrete methods in the context of undersampled MRI reconstruction.

ISBN: 9781369304541Subjects--Topical Terms:

2122814
Applied mathematics.

Off-the-grid compressive imaging.
LDR:02302nmm a2200301 4500 001 2117703
005 20170530090538.5
008 180830s2016 ||||||||||||||||| ||eng d
020 $a 9781369304541
035 $a (MiAaPQ)AAI10181928
035 $a AAI10181928
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ongie, Gregory J. $3 3279494
245 1 0 $a Off-the-grid compressive imaging.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 134 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
500 $a Adviser: Mathews Jacob.
502 $a Thesis (Ph.D.)--The University of Iowa, 2016.
520 $a In many practical imaging scenarios, including computed tomography and magnetic resonance imaging (MRI), the goal is to reconstruct an image from few of its Fourier domain samples. Many state-of-the-art reconstruction techniques, such as total variation minimization, focus on discrete "on-the-grid" modeling of the problem both in spatial domain and Fourier domain. While such discrete-to-discrete models allow for fast algorithms, they can also result in sub-optimal sampling rates and reconstruction artifacts due to model mismatch. Instead, this thesis presents a framework for "off-the-grid", i.e. continuous domain, recovery of piecewise smooth signals from an optimal number of Fourier samples. The main idea is to model the edge set of the image as the level-set curve of a continuous domain band-limited function. Sampling guarantees can be derived for this framework by investigating the algebraic geometry of these curves. This model is put into a robust and efficient optimization framework by posing signal recovery entirely in Fourier domain as a structured low-rank (SLR) matrix completion problem. An efficient algorithm for this problem is derived, which is an order of magnitude faster than previous approaches for SLR matrix completion. This SLR approach based on off-the-grid modeling shows significant improvement over standard discrete methods in the context of undersampled MRI reconstruction.
590 $a School code: 0096.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Electrical engineering. $3 649834
650 4 $a Medical imaging. $3 3172799
690 $a 0364
690 $a 0544
690 $a 0574
710 2 $a The University of Iowa. $b Applied Mathematical and Computational Sciences. $3 3279491
773 0 $t Dissertation Abstracts International $g 78-05B(E).
790 $a 0096
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10181928