Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector

TL;DR

This paper introduces an adaptive 3D imaging method using wavelet trees and Hadamard multiplexing with a single-photon detector, enabling high-resolution 3D images with reduced measurement and reconstruction time.

Contribution

It presents a novel multi-resolution photon counting 3D imaging technique combining wavelet trees and Hadamard multiplexing for efficient high-resolution imaging.

Findings

Achieved 3D imaging at 512x512 resolution within 17 seconds

Significantly increased detected power through Hadamard multiplexing

Reduced measurements and reconstruction time via wavelet-tree-based edge prediction

Abstract

Photon counting 3D imaging allows to obtain 3D images with single-photon sensitivity and sub-ns temporal resolution. However, it is challenging to scale to high spatial resolution. In this work, we demonstrate a photon counting 3D imaging technique with short-pulsed structured illumination and a single-pixel photon counting detector. The proposed multi-resolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and edges at successfully finer resolution sampled by Hadamard multiplexing along the wavelet trees. The detected power is significantly increased thanks to the Hadamard multiplexing. Both the required measurements and the reconstruction time can be significantly reduced by performing wavelet-tree-based regions of edges predication and Hadamard demultiplexing, which makes the proposed technique suitable for scenes with high spatial resolution. The experimental results indicate that a 3D image at resolution up to 512*512 pixels can be acquired and retrieved with practical time as low as 17 seconds.