Compressive depth map acquisition using a single photon-counting detector: Parametric signal processing meets sparsity

Colaço, Andrea; Kirmani, Ahmed; Howland, Gregory A.; Howell, John C.; Goyal, Vivek K

doi:10.1109/cvpr.2012.6247663

Cited by 37 publications

(32 citation statements)

References 21 publications

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“…The recovery method is a modified version of the model-based algorithm [19] that includes a total variation (TV) regularization term in the signal estimation step. In [20] and [21] a single photon counting detector is used as single pixel. The system and methods presented in [20] derive from those in [16], where the photodetector has been substituted by a single photon counting detector and the SLM by a DMD.…”

Section: Related Workmentioning

confidence: 99%

“…In [20] and [21] a single photon counting detector is used as single pixel. The system and methods presented in [20] derive from those in [16], where the photodetector has been substituted by a single photon counting detector and the SLM by a DMD. The depth recovery framework also comes from [16], inheriting its intrinsic limitations, e.g., restriction to frontoparallel facets.…”

Section: Related Workmentioning

confidence: 99%

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A Compressed Sensing Framework for Accurate and Robust Waveform Reconstruction and Phase Retrieval Using the Photonic Mixer Device

2015

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A common way of performing phase-shift-based time-of-flight imaging combines the emission of a continuous-wave (CW) illumination signal with correlation with some reference signals at the detector array. This is the case for the well-known photonic mixer device (PMD), which correlates against displaced versions of the illumination control signal, at known phase shifts, and requires only four correlation values to estimate the phase shift. The main drawback of such approaches is that they require the assumption of nonrealistic hypothesis regarding the sensing process, leading to simple sensing models that, despite allowing fast depth estimation from few acquisitions, often ignore relevant considerations for real operation, leaving the door open for systematic errors that affect the final depth accuracy. Typical examples are ignoring the effect of the illumination devices on the final shape of the illumination signal, supposing a sinusoidal reference signal at pixel level, or not accounting for multipath effects. In this work, we present a novel framework for PMD-based signal acquisition and recovery that exploits the sparsity of CW illumination signals in the frequency domain to provide accurate reconstruction of the illumination waveforms as received by the PMD pixels. Our method is extremely robust to signal distortion and noise, since no assumption is made on the illumination signal, other than being a periodic signal. Our approach ensures that no valuable information is lost during the sensing process and allows, therefore, accurate phase shift estimation in a wider range of operation conditions, getting rid of unrealistic assumptions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Compressed Sensing Framework for Accurate and Robust Waveform Reconstruction and Phase Retrieval Using the Photonic Mixer Device

2015

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“…In recent years, single-pixel imaging system [6][7][8][9][10][11][12][13] has been paid more and more attention and has been successfully applied in many fields, such as threedimensional imaging [6][7][8][9], optical security [10] and so on [11]. Single-pixel imaging system needs to modulate the illumination light intensity, and the reflected light is detected by single-pixel no-scanning detector.…”

Section: Introductionmentioning

confidence: 99%

Detecting the Trajectory of Moving Object for Single-Pixel Imaging System

Hui

Chen

et al. 2018

MATEC Web Conf.

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Abstract. In order to get the trajectory of moving object using single-pixel imaging system, an algorithm is proposed. The same pseudorandom masks are employed to illuminate the different time scene. A time weighted sum of the background correction signals is employed to get the trajectory information using compressed sensing (CS) method. In ideal situation, we can obtain other parameters (e.g., speed, orientation) besides the trajectory. However, the reflective intensity of the object can be change due to the reflective angle change caused by the motion in some situations. This will mislead for achieving the speed, orientation parameters. In order to eliminate this effect, a division method is utilized. At last, the computer simulation results prove the effect validity of the proposed algorithm.

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“…The most photonefficient imagers use single-photon avalanche diode (SPAD) detectors [8]. See [9] for a discussion of compressive methods [10][11][12][13] and earlier efforts in SPAD-based 3D imaging [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Computational 3D and reflectivity imaging with high photon efficiency

Shin

Kirmani

Goyal

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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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.

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Compressive depth map acquisition using a single photon-counting detector: Parametric signal processing meets sparsity

Abstract: Active range acquisition systems such as light detection and ranging (LIDAR)

Cited by 37 publications

References 21 publications

A Compressed Sensing Framework for Accurate and Robust Waveform Reconstruction and Phase Retrieval Using the Photonic Mixer Device

A Compressed Sensing Framework for Accurate and Robust Waveform Reconstruction and Phase Retrieval Using the Photonic Mixer Device

Detecting the Trajectory of Moving Object for Single-Pixel Imaging System

Computational 3D and reflectivity imaging with high photon efficiency

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