In recent decades, single-photon detectors have emerged as crucial technologies for 3D remote sensing and biomedical applications. However, extracting
essential parameters from encoded single-photon data presents significant challenges due to the ill-posed nature of parameter reconstruction, leading to high
computational complexity. Robust yet compact algorithms are essential to ensure accuracy and computational efficiency. Furthermore, implementing these
efficient algorithms on reconfigurable hardware processors promotes portability
and real-world applicability.
This thesis addresses these challenges through three interrelated topics that
integrate signal processing on single-photon data, deep learning, and hardware
implementation. For each topic, quantitative comparisons are conducted between
our algorithms and state-of-the-art methods, demonstrating the superiority of our
compact algorithms and hardware architectures.
In the first topic, depth images are reconstructed from 3D point cloud data
captured by a single-photon avalanche diode (SPAD) array, even under extreme
low signal-to-background ratios (0.2, 0.04, and 0.02) per pixel. A low-bit quantization strategy is applied to the 3D DL model to achieve a small model size while
maintaining accuracy.
The second topic focuses on fluorescence lifetime reconstruction of both synthetic data and real data from experiments, utilizing 1D temporal point spread
functions (TPSF) acquired by a photomultiplier tube (PMT) coupled with a timecorrelated single-photon counting (TCSPC) system. A lightweight 1D DL model,
in conjunction with TPSF compression and a customized hardware processor,
facilitates rapid and accurate lifetime image reconstruction. This approach surpasses conventional DL models and non-linear fitting methods in performance.
In the third topic, we accurately reconstruct the blood flow index and coherence factor from autocorrelation functions for a diffuse correlation spectroscopy
(DCS) system using customized 1D DL. A processor implemented on a reconfigurable device paves the way for integrating portable DCS systems in the future.
Through these contributions, this thesis advances the field of single-photon
signal processing by providing compact algorithms and hardware implementations
that improve accuracy, computational efficiency, and portability in single-photon
applications for 3D sensing and biomedical imaging.
| Date of Award | 13 Jan 2025 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | University of Strathclyde |
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| Supervisor | David Li (Supervisor) & Erfu Yang (Supervisor) |
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