Designing miniature computational cameras for photography, microscopy, and artificial intelligence

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dc.contributor.advisorVeeraraghavan, Ashoken_US
dc.creatorBoominathan, Viveken_US
dc.date.accessioned2019-08-01T20:26:09Zen_US
dc.date.available2019-08-01T20:26:09Zen_US
dc.date.created2019-08en_US
dc.date.issued2019-07-25en_US
dc.date.submittedAugust 2019en_US
dc.date.updated2019-08-01T20:26:09Zen_US
dc.description.abstractThe fields of robotics, Internet of things, health-monitoring, neuroscience, and many others have consistently trended toward miniaturization of sensing devices over the past few decades. This miniaturization has enabled new applications in areas such as connected devices, wearables, implantable medical devices, in vivo microscopy and micro-robotics. By integrating visual sensing capabilities in such devices, we can enable improved spatial, temporal and contextual information collection for artificial intelligence and data processing. However, cameras are reaching their limits in size reduction due to restrictions of traditional lensed-optics. If we can combine the design of camera optics and computational algorithms, we can potentially achieve miniaturization beyond traditional optics. In this dissertation, we explore designing unconventional optics coupled with computational algorithms to achieve miniaturization. Recent works have shown the use of flat diffractive optics, placed at a focus distance from the imaging sensor, as a substitute for lensing and the use of computational algorithms to correct and sharpen the images. We take this a step further and place a thin diffractive mask at a very close distance (range of 100s of microns to a millimeter) from the imaging sensor, thereby achieving an even smaller form-factor. Such flat camera geometry calls for new ways of modeling the system, methods to optimize the mask design and computational algorithms to recover high-resolution images. Moreover, retaining the thin geometry, we develop a framework to design optical masks to off-load some of the computational processing to inherently zero-power optical processing. With the developed methods, we demonstrate (1) ultraminiature microscopy, (2) thickness constrained high-resolution imaging, (3) optical Gabor feature extraction, and an example of (4) hybrid optical-electronic computer vision system.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationBoominathan, Vivek. "Designing miniature computational cameras for photography, microscopy, and artificial intelligence." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/106165">https://hdl.handle.net/1911/106165</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/106165en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectLenslessen_US
dc.subjectcameraen_US
dc.subjectminiatureen_US
dc.subjectultraminiatureen_US
dc.subjectphase masken_US
dc.subjectdiffractive opticsen_US
dc.titleDesigning miniature computational cameras for photography, microscopy, and artificial intelligenceen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.majorDSP, Systemsen_US
thesis.degree.nameDoctor of Philosophyen_US
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