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Model-Based Deep Learning For Computational Imaging, Xiaojian Xu
Model-Based Deep Learning For Computational Imaging, Xiaojian Xu
McKelvey School of Engineering Theses & Dissertations
This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.
The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …
Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan
Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan
Doctoral Dissertations
This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data.
Using physical targets and sensors in this scenario would be …