Parametric Poisson process imaging

Shin, Dongeek; Kirmani, Ahmed; Colaço, Andrea; Goyal, Vivek K

doi:10.1109/globalsip.2013.6737075

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Earlier efforts in SPAD-based 3D imaging from on the order of 1 detected ppp are reported in [12]- [14]. The framework presented here improves upon these works in part due to the use of estimated reflectivity.…”

Section: Photon-efficient Computational 3-d and Reflectivitymentioning

confidence: 87%

“…The most photon-efficient TOF imagers-those requiring the fewest photons for accurate imaging-use single-photon avalanche diode (SPAD) detectors [10]. Earlier efforts in SPAD-based 3D imaging from on the order of 1 detected ppp are reported in [11]- [13]. The framework presented here improves upon these works in part due to the use of estimated reflectivity.…”

Section: Introductionmentioning

confidence: 99%

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

Shin

Kirmani

Goyal

et al. 2015

IEEE Trans. Comput. Imaging

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184

167

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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 detectors sensitive to individual photons, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using fixed dwell time per pixel and on the order of one detected photon per pixel averaged over the scene. Our computational image formation method combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3-D structure. Experiments conducted in the presence of strong background light demonstrate that our method is able to accurately recover scene depth and reflectivity, while traditional imaging methods based on maximum likelihood (ML) estimation or approximations thereof lead to noisier images. For depth, performance compares favorably to signal-independent noise removal algorithms such as median filtering or block-matching and 3-D filtering (BM3D) applied to the pixelwise ML estimate; for reflectivity, performance is similar to signal-dependent noise removal algorithms such as Poisson nonlocal sparse PCA and BM3D with variance-stabilizing transformation. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus, our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array.Index Terms-3-D imaging, computational imaging, convex optimization, first-photon imaging, LIDAR, low-light imaging, Poisson noise, single-photon detection, time-of-flight imaging.

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Section: Photon-efficient Computational 3-d and Reflectivitymentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

Shin

Kirmani

Goyal

et al. 2015

IEEE Trans. Comput. Imaging

Self Cite

184

167

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show abstract

“…For this imaging method, it needed to take twice detections of long and short exposure time to determine the best depth estimation of each pixel. Ahmed Kirmani and Dheera Venkatram et al [11][12][13] brought out a mathematical probability model of photon counting process equipped with the single-photon detector. They used the first detected photon to estimate the depth and reflectivity images of target combining the spatial correlation among adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Depth Imaging With Single-Photon Detectors

Feng

Lin

et al. 2017

IEEE Photonics J.

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For active optical imaging, the use of single-photon detectors can greatly improve the detection sensitivity of the system. However, the traditional maximum-likelihood based imaging method needs a long acquisition time to capture clear three-dimensional (3D) image in low light-level. To tackle this problem, we present a novel imaging method for depth estimate, which can obtain the accurate 3D image in a short acquisition time. Our method combines the photon-count statistics with the temporal correlations of the reflected signal. According to the characteristics of the target surface, including the surface reflectivity, our method is capable of adaptively changing the dwell time in each pixel. The experimental results demonstrate that the proposed method can fast obtain the accurate depth image despite the existence of strong background noise.

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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%

“…Its rasterscanning process to collect 1 ppp, however, makes the dwell time at each pixel a random variable. The dwell time is also a random variable when collecting any fixed number of photons per pixel, as in [15,16] for 3D imaging separated from reflectivity. Thus, FPI does not extend naturally to operation using SPAD arrays-since simultaneous measurement implies equal dwell times-and precludes the dramatic speedup in image acquisition that such arrays enable.…”

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.

show abstract

Parametric Poisson process imaging

Cited by 11 publications

References 9 publications

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

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

Adaptive Depth Imaging With Single-Photon Detectors

Computational 3D and reflectivity imaging with high photon efficiency

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