Computational 3D and reflectivity imaging with high photon efficiency

Abstract

Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We introduce a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum likelihood-based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and thus will be useful for rapid, low-power, and noise-tolerant active optical imaging.

Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors

“…We expound upon a framework, introduced in [2], that builds upon an approach initiated in [3], [4]. Like the first-photon imaging (FPI) method of [3], our computational imager distinguishes itself from other previous work by avoiding the formation of histograms and instead using probabilistic modeling at the level of individual detected photons.…”

“…The methods detailed in this paper were presented in preliminary, abbreviated form in [2]. The present manuscript provides additional context (Section I-A), details on derivations (Section III), performance bounds (Appendix), and experimental results that do not appear in [2] (Section VI).…”

“…The present manuscript provides additional context (Section I-A), details on derivations (Section III), performance bounds (Appendix), and experimental results that do not appear in [2] (Section VI). In particular, comparisons to Poisson non-local sparse PCA (NLSPCA) [22] and block-matching and 3D filtering (BM3D) [23]-with variance-stabilizing transform (VST) for reflectivity [24]replace the use of less-sophisticated bilateral filtering [25] in the preliminary work.…”

“…An alternate derivation of this PDF that includes the time resolution of the detector is given in [28].…”

Computational single-photon depth imaging without transverse regularization

“…Very recently, computational imaging frameworks have been introduced to process photon detection data without treating a detection-time histogram as approximating a flux waveform, and using regularization predicated on piecewise smoothness in the transverse dimensions [6][7][8][9][10]. For natural scenes, accurate results have been demonstrated from as little as 1 detected photon per pixel, even in the presence of significant ambient light.…”