Photon-limited depth and reflectivity imaging with sparsity regularization

Kang, Yan; Li, Lifei; Duan, Xuejie; Zhang, Tong‐Yi; Li, Miaosi; Zhao, Wei

doi:10.1016/j.optcom.2017.01.032

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…This algorithm assumes the presence of one peak and known corrupted pixel positions. The algorithm in [43] assumes similar regularizations and we expect its performance to be of the same order of magnitude as for RDI-TV.…”

Section: B Comparison Algorithmsmentioning

confidence: 99%

Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Halimi

Tobin

McCarthy

et al. 2020

IEEE Trans. Comput. Imaging

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The challenges of real world applications of the laser detection and ranging (Lidar) three-dimensional (3-D) imaging require specialized algorithms. In this paper, a new reconstruction algorithm for single-photon 3-D Lidar images is presented that can deal with multiple tasks. For example, when the return signal contains multiple peaks due to imaging semitransparent surfaces, or when imaging through obscurants such as scattering media. A generalization to the multidimensional case, including multispectral and multitemporal 3-D images, is also provided. The approach is based on the minimization of a cost function accounting for Poissonian observations of the single-photon data, the nonlocal spatial correlations between pixels and the small number of depth layers inside the observed range window. An alternating direction method of multipliers that offers good convergence properties is used to solve this minimization problem. The resulting algorithm is validated on synthetic and real data and in challenging realistic scenarios including sparse photon regimes for fast imaging, the presence of high background due to obscurants, and the joint processing of multispectral and/or multitemporal data.

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Section: B Comparison Algorithmsmentioning

confidence: 99%

Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Halimi

Tobin

McCarthy

et al. 2020

IEEE Trans. Comput. Imaging

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“…In this paper, a multiple-input multiple-output (MIMO) scenario for NF THz imaging systems is considered. Unlike many existing works focusing on target domain sparsity [14,15], our focus is not on the target domain sparsity but the physical layer sparsity. The CS technique is employed here to significantly reduce the number of physical antennas and spatial sampling points.…”

Section: Introductionmentioning

confidence: 99%

Fourier Compatible Near-Field Multiple-Input Multiple-Output Terahertz Imaging With Sparse Non-Uniform Apertures

Molaei¹,

Skouroliakou

et al. 2021

IEEE Access

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“…But, this is not same in all cases, such as photo-materials. Apart from bio-imaging and wavefront sensing, low-light imaging has also been reported in digital holography (DH) [7][8][9], threedimensional imaging [10,11], optical cryptography [12][13][14], * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Low-light phase imaging using in-line digital holography and the transport of intensity equation

Gupta

Nishchal

2021

J. Opt.

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In one of our recent studies, we have shown that a large defocusing distance in the transport of intensity equation (TIE) is required to retrieve the phase information in low-light conditions (OSA Continuum 3 (2020) 236). It has been found that image misalignment issues come into the picture due to translation of the camera for large distances, which creates artifacts in phase recovery. The issue of misalignment can be overcome if, instead of the object, we use the digital hologram of the object for the required defocused intensities and then apply the TIE. In this paper, we demonstrate low-light phase imaging by combining digital holography with the TIE. We reconstruct the required multiple intensity distributions from the captured in-line digital hologram. To implement the low-level light illumination, a variable neutral density filter has been used while recording the in-line digital hologram. Simulation and experimental results with different objects (United States air-force chart and glue drop) are presented. To simulate the low-light conditions, a Poisson distribution based photon-counting imaging technique has been applied.

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Photon-limited depth and reflectivity imaging with sparsity regularization

Cited by 15 publications

References 25 publications

Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Robust Restoration of Sparse Multidimensional Single-Photon LiDAR Images

Fourier Compatible Near-Field Multiple-Input Multiple-Output Terahertz Imaging With Sparse Non-Uniform Apertures

Low-light phase imaging using in-line digital holography and the transport of intensity equation

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