Date of Award

Spring 2025

Document Type

Thesis (Master's)

Department or Program

Computer Science

First Advisor

Adithya Pediredla

Second Advisor

Alberto Quattrini Li

Third Advisor

Lorie Loeb

Abstract

High-speed imaging systems are used to study the motion of particles in fluid dynamics, capture shockwave propagation, and analyze the behavior of materials under stress. Observing such rapid phenomena allows for a deeper understanding of complex systems that otherwise are invisible to the human eye. However, traditional imaging systems face significant challenges when capturing high-speed events. These systems are often constrained by bandwidth limitations, preventing them from acquiring data at the necessary rates to capture fast-moving objects with the required spatial detail. We incorporate generative AI to reconstruct the images to overcome bandwidth limitations. By conditioning these models on the limited captured data, we reconstruct the images, with a primary focus on spatial resolution. We use a single-pixel detector capable of acquiring data at rates up to 2 gigahertz (2 billion measurements per second) for high-speed data capture. This is combined with a high-speed galvanometer mirror system, enabling fast scanning, specifically following Lissajous curves. The system design focuses on maximizing data capture within the bandwidth constraints while leveraging sparse measurements to reconstruct spatial images. Although current work has demonstrated image reconstruction from sparse data for static images, the approach holds promise for reconstructing dynamic events in the future. This research paves the way for next-generation high-speed imaging systems capable of achieving spatial and temporal resolution.

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