國立虎尾科技大學 |

Single-Shot 3D Microscopy: Optics and Algorithms Co-Design.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Single-Shot 3D Microscopy: Optics and Algorithms Co-Design./
作者:	Liu, Fanglin Linda.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	106 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29208849
ISBN:	9798351476513

Single-Shot 3D Microscopy: Optics and Algorithms Co-Design.
Liu, Fanglin Linda.

Single-Shot 3D Microscopy: Optics and Algorithms Co-Design. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 106 p.

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (D.Eng.)--University of California, Berkeley, 2022.

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

Computational imaging involves simultaneously designing optical hardware and reconstruction software. Such a co-design framework brings together the best of both worlds for an imaging system. The goal is to develop a high-speed, high-resolution, and large field-of-view microscope that can detect 3D fluorescence signals from single image acquisition. To achieve this goal, I propose a new method called Fourier DiffuserScope, a single-shot 3D fluorescent microscope that uses a phase mask (i.e., a diffuser with random microlenses) in the Fourier plane to encode 3D information, then computationally reconstructs the volume by solving a sparsity-constrained inverse problem.In this dissertation, I will discuss the design principles of the Fourier DiffuserScope from three perspectives: first-principles optics, compressed sensing theory, and physics-based machine learning. First, in the heuristic design, the phase mask consists of randomly placed microlenses with varying focal lengths; the random positions provide a larger field-of-view compared to a conventional microlens array, and the diverse focal lengths improve the axial depth range. I then build an experimental system that achieves < 3 μm lateral and 4 μm axial resolution over a 1000x1000x280 μm3 volume. Lastly, we use a differentiable forward model of Fourier DiffuserScope in conjunction with a differentiable reconstruction algorithm to jointly optimize both the phase mask surface profile and the reconstruction parameters. We validate our method in 2D and 3D single-shot imaging, where the optimized diffuser demonstrates improved reconstruction quality compared to previous heuristic designs.

ISBN: 9798351476513Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

3D imaging

Single-Shot 3D Microscopy: Optics and Algorithms Co-Design.
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300 $a 106 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
500 $a Advisor: Waller, Laura.
502 $a Thesis (D.Eng.)--University of California, Berkeley, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Computational imaging involves simultaneously designing optical hardware and reconstruction software. Such a co-design framework brings together the best of both worlds for an imaging system. The goal is to develop a high-speed, high-resolution, and large field-of-view microscope that can detect 3D fluorescence signals from single image acquisition. To achieve this goal, I propose a new method called Fourier DiffuserScope, a single-shot 3D fluorescent microscope that uses a phase mask (i.e., a diffuser with random microlenses) in the Fourier plane to encode 3D information, then computationally reconstructs the volume by solving a sparsity-constrained inverse problem.In this dissertation, I will discuss the design principles of the Fourier DiffuserScope from three perspectives: first-principles optics, compressed sensing theory, and physics-based machine learning. First, in the heuristic design, the phase mask consists of randomly placed microlenses with varying focal lengths; the random positions provide a larger field-of-view compared to a conventional microlens array, and the diverse focal lengths improve the axial depth range. I then build an experimental system that achieves < 3 μm lateral and 4 μm axial resolution over a 1000x1000x280 μm3 volume. Lastly, we use a differentiable forward model of Fourier DiffuserScope in conjunction with a differentiable reconstruction algorithm to jointly optimize both the phase mask surface profile and the reconstruction parameters. We validate our method in 2D and 3D single-shot imaging, where the optimized diffuser demonstrates improved reconstruction quality compared to previous heuristic designs.
590 $a School code: 0028.
650 4 $a Computer science. $3 573171
650 4 $a Electrical engineering. $3 596380
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653 $a 3D imaging
653 $a Fluorescence microscopy
653 $a Physics-based machine learning
653 $a Machine learning
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793 $a English
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