Grace Kuo

Publications by Grace Kuo

2021

Kristina Monakhova; Vi Tran; Grace Kuo; Laura Waller

Untrained networks for compressive lensless photography Journal Article

In: Opt. Express, vol. 29, no. 13, pp. 20913–20929, 2021.

Links | BibTeX

2020

Grace Kuo

Exploiting Randomness in Computational Cameras and Displays PhD Thesis

EECS Department, University of California, Berkeley, 2020.

Abstract | Links | BibTeX

@phdthesis{Kuo:EECS-2020-218,

title = {Exploiting Randomness in Computational Cameras and Displays},

author = {Grace Kuo},

url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-218.html},

year  = {2020},

date = {2020-12-01},

number = {UCB/EECS-2020-218},

school = {EECS Department, University of California, Berkeley},

abstract = {Despite its desirability, capturing and displaying higher dimensional content is still a novelty since image sensors and display panels are inherently 2D. A popular option is to use scanning mechanisms to sequentially capture 3D data or display content at a variety of depths. This approach is akin to directly measuring (or displaying) the content of interest, which has low computational cost but sacrifices temporal resolution and requires complex physical hardware with moving parts. The exacting specifications on the hardware make it challenging to miniaturize these optical systems for demanding applications such as neural imaging in animals or head-mounted augmented reality displays. 

 

In this dissertation, I propose moving the burden of 3D capture from hardware into computation by replacing the physical scanning mechanisms with a simple static diffuser (a transparent optical element with pseudorandom thickness) and formulating image recovery as an optimization problem. First, I highlight the versatility of the diffuser by showing that it can replace a lens to create an easy-to-assemble, compact camera that is robust to missing pixels; although the raw data is not intelligible by a human, it contains information that we extract with optimization using an efficient physically-based model of the optics. Next, I show that the randomness of the diffuser makes the system well-suited for compressed sensing; we leverage this to recover 3D volumes from a single acquisition of raw data. Additionally, I extend our lensless 3D imaging system to fluorescence microscopy and introduce a new diffuser design with improved noise performance. Finally, I show how incorporating the diffuser in a 3D holographic display expands the field-of-view, and I demonstrate state-of-the-art performance by using perceptually inspired loss functions when optimizing the display panel pattern. These results show how randomness in the optical system in conjunction with optimization-based algorithms can both improve the physical form factor and expand the capabilities of cameras, microscopes, and displays.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Despite its desirability, capturing and displaying higher dimensional content is still a novelty since image sensors and display panels are inherently 2D. A popular option is to use scanning mechanisms to sequentially capture 3D data or display content at a variety of depths. This approach is akin to directly measuring (or displaying) the content of interest, which has low computational cost but sacrifices temporal resolution and requires complex physical hardware with moving parts. The exacting specifications on the hardware make it challenging to miniaturize these optical systems for demanding applications such as neural imaging in animals or head-mounted augmented reality displays.

In this dissertation, I propose moving the burden of 3D capture from hardware into computation by replacing the physical scanning mechanisms with a simple static diffuser (a transparent optical element with pseudorandom thickness) and formulating image recovery as an optimization problem. First, I highlight the versatility of the diffuser by showing that it can replace a lens to create an easy-to-assemble, compact camera that is robust to missing pixels; although the raw data is not intelligible by a human, it contains information that we extract with optimization using an efficient physically-based model of the optics. Next, I show that the randomness of the diffuser makes the system well-suited for compressed sensing; we leverage this to recover 3D volumes from a single acquisition of raw data. Additionally, I extend our lensless 3D imaging system to fluorescence microscopy and introduce a new diffuser design with improved noise performance. Finally, I show how incorporating the diffuser in a 3D holographic display expands the field-of-view, and I demonstrate state-of-the-art performance by using perceptually inspired loss functions when optimizing the display panel pattern. These results show how randomness in the optical system in conjunction with optimization-based algorithms can both improve the physical form factor and expand the capabilities of cameras, microscopes, and displays.

Fanglin Linda Liu; Grace Kuo; Nick Antipa; Kyrollos Yanny; Laura Waller

Fourier DiffuserScope: single-shot 3D Fourier light field microscopy with a diffuser Journal Article

In: Opt. Express, vol. 28, no. 20, pp. 28969–28986, 2020.

Abstract | Links | BibTeX