Reconstruction of high-resolution depth profiling from single-photon data based on PCA

Hui Wang; Su Qiu; Taoran Lu

doi:10.1117/12.2688550

28 November 2023 Reconstruction of high-resolution depth profiling from single-photon data based on PCA

Hui Wang, Su Qiu, Taoran Lu

Proceedings Volume 12772, Real-time Photonic Measurements, Data Management, and Processing VII; 127720J (2023) https://doi.org/10.1117/12.2688550
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

The high sensitivity and picosecond temporal resolution of single-photon avalanche diode (SPAD) make it the preferred single-photon detector in extreme imaging environments. Extreme imaging environments (e.g., underwater high-scattering environments) usually result in low signal-to-noise ratios of the acquired single-photon data, which leads to poor quality of image reconstruction, so it is necessary to propose a high-resolution single-photon three-dimensional reconstruction algorithm for extreme imaging environments. Principal component analysis (PCA) is widely used and robust, which is suitable for dimensionality reduction and noise reduction processing of single-photon data with sparse and noisy characteristics. Under the premise that the target data has a strong correlation with the background and random noise, the target feature extraction of the single-photon data is carried out by PCA, the principal components are used to reconstruct the original data, the relative position and size of the original data are effectively retained, the redundant information is removed, and the single-photon data is reconstructed using cross-correlation and ManiPoP algorithms to achieve high-resolution single-photon depth profile reconstruction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui Wang, Su Qiu, and Taoran Lu "Reconstruction of high-resolution depth profiling from single-photon data based on PCA", Proc. SPIE 12772, Real-time Photonic Measurements, Data Management, and Processing VII, 127720J (28 November 2023); https://doi.org/10.1117/12.2688550

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