Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions using less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the advantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.

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Learning-based compressed sensing algorithms are popularly used for recovering the underlying datacube of snapshot compressive temporal imaging (SCTI), which is a novel technique for recording temporal data in a single exposure. Despite providing fast processing and high reconstruction performance, most deep-learning approaches are merely considered a substitute for analytical-modeling-based reconstruction methods. In addition, these methods often presume the ideal behaviors of optical instruments neglecting any deviation in the encoding and shearing processes. Consequently, these approaches provide little feedback to evaluate SCTI’s hardware performance, which limits the quality and robustness of reconstruction. To overcome these limitations, we develop a new end-to-end convolutional neural network—termed the deep high-dimensional adaptive net (D-HAN)—that provides multi-faceted process-aware supervision to an SCTI system. The D-HAN includes three joint stages: four dense layers for shearing estimation, a set of parallel layers emulating the closed-form solution of SCTI’s inverse problem, and a U-net structure that works as a filtering step. In system design, the D-HAN optimizes the coded aperture and establishes SCTI’s sensing geometry. In image reconstruction, D-HAN senses the shearing operation and retrieves a three-dimensional scene. D-HAN-supervised SCTI is experimentally validated using compressed optical-streaking ultrahigh-speed photography to image the animation of a rotating spinner at an imaging speed of 20 thousand frames per second. The D-HAN is expected to improve the reliability and stability of a variety of snapshot compressive imaging systems.

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Compressive spectral depth imaging (CSDI) is an emerging technology aiming to reconstruct spectral and depth information of a scene from a limited set of two-dimensional projections. CSDI architectures have conventionally relied on stereo setups that require the acquisition of multiple shots attained via dynamically programmable spatial light modulators (SLM). This work proposes a snapshot CSDI architecture that exploits both phase and amplitude modulation and uses a single image sensor. Specifically, we modulate the spectral-depth information in two steps. Firstly, a deformable mirror (DM) is used as a phase modulator to induce a focal length sweeping while simultaneously introducing a controlled aberration. The phase-modulated wavefront is then spatially modulated and spectrally dispersed by a digital micromirror device (DMD) and a prism, respectively. Therefore, each depth plane is modulated by a variable phase and binary code. Complimentary, we also propose a computational methodology to recover the underlying spectral depth hypercube efficiently. Through simulations and our experimental proof-of-concept implementation, we demonstrate that the proposed computational imaging system is a viable approach to capture spectral-depth hypercubes from a single image.

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