Adaptive compressed photon counting 3D imaging based on wavelet trees and Hadamard multiplexing

A photon counting 3D imaging system with short-pulsed structured illumination and a single-pixel photon counting detector is built. The proposed multiresolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully finer resolution sampled by Hadamard multiplexing along with the wavelet trees. The detected power is significant increased thanks to the Hadamard multiplexing. Both the required measurements and the reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes with high spatial resolution. Since the depth map is retrieved through a linear inverse Hadamard transform instead of the computational intensive optimization problems performed in CS, the time consumed to retrieve the depth map can be also reduced, and thus it will be suitable for applications of real-time compressed 3D imaging such as object tracking. Even though the resolution of the final 3D image can be high, the number of measurements remains small due to the adaptivity of the wavelet-trees-based sampling strategy. The adaptive sampling technique is quality oriented, allowing more control over the image quality. The experimental results indicate that both the intensity image and depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical times as low as 17 seconds.