Leveraging Physics-based Models in Data-driven Computational Imaging

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dc.contributor.committeeMemberVeeraraghavan, Ashoken_US
dc.contributor.committeeMemberBaraniuk, Richarden_US
dc.contributor.committeeMemberShrivastava, Anshumalien_US
dc.creatorChen, Georgeen_US
dc.date.accessioned2019-05-16T16:30:58Zen_US
dc.date.available2020-05-01T05:01:10Zen_US
dc.date.created2019-05en_US
dc.date.issued2019-04-19en_US
dc.date.submittedMay 2019en_US
dc.date.updated2019-05-16T16:30:58Zen_US
dc.description.abstractDeep Learning (DL) has revolutionized various applications in computational imaging and computer vision. However, existing DL frameworks are mostly data-driven, which largely disregards decades of prior work that focused on signal processing theory and physics-based models. As a result, many DL based image reconstruction methods generate eye-pleasing results but faces strong drawbacks, including 1) output not being physically correct, 2) requiring large datasets with labor-intensive annotations. In the thesis, we propose several computational imaging frameworks that leverage both physics-based models and data-driven deep learning. By formulating the physical model as an integrated and differentiable layer of the larger learning networks, we are able to a) constraint the results to be closer to the physical reality, b) perform self-supervised network training using the physical constraints as loss functions, avoiding manually labeled data, and c) develop true end-to-end imaging systems with jointly optimized front-end sensors and back-end algorithms. In particular, we show that the proposed approach is suitable for a wide range of applications, including motion de-blurring, 3D imaging and super-resolution microscopy.en_US
dc.embargo.terms2020-05-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationChen, George. "Leveraging Physics-based Models in Data-driven Computational Imaging." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/105337">https://hdl.handle.net/1911/105337</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/105337en_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.subjectComputational Photographyen_US
dc.subjectComputer Visionen_US
dc.subjectDeep Learningen_US
dc.titleLeveraging Physics-based Models in Data-driven Computational Imagingen_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.nameDoctor of Philosophyen_US
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