Matching Items (2)
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Description
Light field imaging is limited in its computational processing demands of high

sampling for both spatial and angular dimensions. Single-shot light field cameras

sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing

incoming rays onto a 2D sensor array. While this resolution can be recovered using

compressive sensing, these iterative solutions are slow

Light field imaging is limited in its computational processing demands of high

sampling for both spatial and angular dimensions. Single-shot light field cameras

sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing

incoming rays onto a 2D sensor array. While this resolution can be recovered using

compressive sensing, these iterative solutions are slow in processing a light field. We

present a deep learning approach using a new, two branch network architecture,

consisting jointly of an autoencoder and a 4D CNN, to recover a high resolution

4D light field from a single coded 2D image. This network decreases reconstruction

time significantly while achieving average PSNR values of 26-32 dB on a variety of

light fields. In particular, reconstruction time is decreased from 35 minutes to 6.7

minutes as compared to the dictionary method for equivalent visual quality. These

reconstructions are performed at small sampling/compression ratios as low as 8%,

allowing for cheaper coded light field cameras. We test our network reconstructions

on synthetic light fields, simulated coded measurements of real light fields captured

from a Lytro Illum camera, and real coded images from a custom CMOS diffractive

light field camera. The combination of compressive light field capture with deep

learning allows the potential for real-time light field video acquisition systems in the

future.
ContributorsGupta, Mayank (Author) / Turaga, Pavan (Thesis advisor) / Yang, Yezhou (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
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Description

This study was conducted to determine the difference in compressive strength between decayed and healthy teeth. The teeth were subjected to a compressive force to simulate the process of mastication. This was done to show that healthy teeth would be better at handling these compressive forces since they have more

This study was conducted to determine the difference in compressive strength between decayed and healthy teeth. The teeth were subjected to a compressive force to simulate the process of mastication. This was done to show that healthy teeth would be better at handling these compressive forces since they have more enamel. 26 teeth samples were collected (19 molars, 4 canines, and 3 premolars) evenly distributed between healthy and decayed. The samples were dimensionally analyzed using electronic calipers and then categorized as either decayed or healthy. The samples were then placed in a nut bolt with epoxy so that the samples could be compressed. Each sample was recorded on video while they were being exposed to the compressive force. This was done to observe how the samples were coming in contact with the Shimadzu compression machine. The amount of force that was required for the samples to exhibit the first point of breakage was recorded by the machine in pounds of force. Various analyses were conducted to determine relationships between several variables. The results showed that as the total and occlusal surface area increased, so did the amount of force the samples could absorb before breakage. As the machine came in contact with more cusps among the molar samples, those samples were able to absorb a larger compressive force. The average force that the decayed and healthy molar samples endured before breakage was roughly even, with the decayed samples average being slightly greater.

ContributorsHenscheid, Keaton J (Author) / Quaranta, Kimberly (Thesis director) / Peoples, Samuel (Committee member) / College of Health Solutions (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05