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2D and 3D Computational Optical Imag...

Nguyen, Thanh.

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2D and 3D Computational Optical Imaging Using Deep Convolutional Neural Networks (DCNNs).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	2D and 3D Computational Optical Imaging Using Deep Convolutional Neural Networks (DCNNs)./
作者:	Nguyen, Thanh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	200 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
Contained By:	Dissertations Abstracts International80-08B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13422967
ISBN:	9780438867321

2D and 3D Computational Optical Imaging Using Deep Convolutional Neural Networks (DCNNs).
Nguyen, Thanh.

2D and 3D Computational Optical Imaging Using Deep Convolutional Neural Networks (DCNNs). - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 200 p.

Source: Dissertations Abstracts International, Volume: 80-08, Section: B.

Thesis (Ph.D.)--The Catholic University of America, 2019.

This item must not be added to any third party search indexes.

Traditionally, neural networks (NNs) are used to model a highly complex system architecture that consists of unknown parameters. These parameters can be trained and adapted for unseen inputs to match the correct outputs based on training the system on known matched inputs and outputs. Deep convolutional neural networks (DCNNs), a branch of NNs, offers an encouraging framework providing state-of-the-art performance for many of the image processing problems. In this dissertation, the novel techniques based DCNN are presented that provide the solutions for the inverse problems to compute 2-dimensional phase map distribution and 3-dimensional distribution of refraction index. Instead of using optical complex model-based techniques, the data-driven reconstruction techniques based on machine learning, in particular deep learning (DL), have gained tremendous success in solving complex inverse problems. From that, the reconstruction of 2D phase and 3D refraction index distribution relies on large datasets to 'learn' the underlying inverse problem.

ISBN: 9780438867321Subjects--Topical Terms:

649834
Electrical engineering.

2D and 3D Computational Optical Imaging Using Deep Convolutional Neural Networks (DCNNs).
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506 $a This item must not be sold to any third party vendors.
520 $a Traditionally, neural networks (NNs) are used to model a highly complex system architecture that consists of unknown parameters. These parameters can be trained and adapted for unseen inputs to match the correct outputs based on training the system on known matched inputs and outputs. Deep convolutional neural networks (DCNNs), a branch of NNs, offers an encouraging framework providing state-of-the-art performance for many of the image processing problems. In this dissertation, the novel techniques based DCNN are presented that provide the solutions for the inverse problems to compute 2-dimensional phase map distribution and 3-dimensional distribution of refraction index. Instead of using optical complex model-based techniques, the data-driven reconstruction techniques based on machine learning, in particular deep learning (DL), have gained tremendous success in solving complex inverse problems. From that, the reconstruction of 2D phase and 3D refraction index distribution relies on large datasets to 'learn' the underlying inverse problem.
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650 4 $a Optics. $3 517925
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