Shiyun Xu

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-160

May 20, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-160.pdf

Visual aberrations, such as farsightedness and nearsightedness, have been a prevalent prob- lem around the world for many years. A common way to correct these problems is wearing eyeglasses, which can be cumbersome or infeasible for certain groups of people. An alterna- tive approach is to utilize specialized hardware to convert a normal display to a light-field display and do computation in a specific way such that the displayed image will become clear when it reaches the user’s retina.

Recent studies have shown various computing methods on different hardware. However, most of them focus on the quality of the perceived image. In this report, we want to examine these algorithms from a practical point of view, focusing on the tradeoff between runtime and quality. We first propose our design and implementation of two microlens-based prefiltering algorithms and then speed up a few selected algorithms via parallelism to achieve real-time performance.

Advisors: Brian A. Barsky

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BibTeX citation:

@mastersthesis{Xu:EECS-2022-160,
    Author= {Xu, Shiyun},
    Title= {Towards a Real-Time Vision Correcting Display},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-160.html},
    Number= {UCB/EECS-2022-160},
    Abstract= {Visual aberrations, such as farsightedness and nearsightedness, have been a prevalent prob- lem around the world for many years. A common way to correct these problems is wearing eyeglasses, which can be cumbersome or infeasible for certain groups of people. An alterna- tive approach is to utilize specialized hardware to convert a normal display to a light-field display and do computation in a specific way such that the displayed image will become clear when it reaches the user’s retina.

Recent studies have shown various computing methods on different hardware. However, most of them focus on the quality of the perceived image. In this report, we want to examine these algorithms from a practical point of view, focusing on the tradeoff between runtime and quality. We first propose our design and implementation of two microlens-based prefiltering algorithms and then speed up a few selected algorithms via parallelism to achieve real-time performance.},
}

EndNote citation:

%0 Thesis
%A Xu, Shiyun 
%T Towards a Real-Time Vision Correcting Display
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 20
%@ UCB/EECS-2022-160
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-160.html
%F Xu:EECS-2022-160