Dual-parameter estimation algorithm for Gm-APD Lidar depth imaging through smoke

Zhang, Yinbo; Li, Sining; Sun, Jianfeng; Liu, Di; Zhang, Xin; Yang, Xianhui; Zhou, Xin

doi:10.1016/j.measurement.2022.111269

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…The shape parameter and inverse scale parameter of Gamma distribution are considered as scattering times and attenuation coefficients, respectively. In our recent work, we verified the defogging imaging ability of Gamma model estimation algorithm in indoor and outdoor experiments [21][22][23] . However, we find that the shape parameters and inverse scale parameters estimated by each pixel are quite different, especially in outdoor experiments using a single-photon detector array through fog.…”

Section: Introductionmentioning

confidence: 64%

Estimation of smoke echo signal using single-photon lidar

Zhang

Sun

et al. 2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

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The non-uniform distribution of smoke and laser spot seriously limits the imaging ability of single-photon lidar through smoke. To this end, based on the collision theory between photons and smoke particles, this paper establishes the time-domain distribution model of scattered photons (Gamma), which considers the lidar system parameters and the characteristics of smoke particles. The shape parameter and inverse scale parameter are defined as the maximum scattering number K and the average scattering number β, respectively. The indoor test effectively illustrates the estimation ability of the model for smoke. The parameter estimation results show that the average scattering number β and the maximum scattering number K increase linearly with the increase of attenuation coefficient. This study is of great significance for the suppression of smoke backscattering and is expected to improve the weather adaptability of single-photon lidar.

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Section: Introductionmentioning

confidence: 64%

Estimation of smoke echo signal using single-photon lidar

Zhang

Sun

et al. 2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

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“…26 for detailed equipment parameters and experimental instructions. We reconstruct depth images of targets under different attenuation lengths and statistical frames, and compare the reconstruction results with those obtained using traditional peak selection algorithm (PSA), all parameter estimation algorithm (APEA) [23] , and dual-parameter estimation algorithm (DPEA) [26] . Meanwhile, we evaluated the reconstructed images using two metrics: target recovery (TR) and relative average ranging error (RARE) [24] .…”

Section: Results and Analysismentioning

confidence: 99%

“…In our previous work [26] , we used the CWT algorithm to search for smoke intervals in the histogram data. The algorithm determines the smoke distribution interval by calculating the peak position of the similarity curve between the input signal and mother wavelet.…”

Section: Fast Search For the Initial Distribution Interval Of Smokementioning

confidence: 99%

“…(2) A sliding window operation is used to superimpose the histograms in the window h to obtain a set of signal photon-enhanced histograms l S . The dual-parameter estimation algorithm (DPEA) [26] is then used to estimate the target distance for each histogram. And the estimation results are expanded to obtain a depth image Z l (Nr × Nc) at scale l.…”

Section: Edge-guided Establishment Of Multi-scale Superpixelsmentioning

confidence: 99%

“…To mitigate limitations in imaging through or within smoky scattering media, numerous researchers have conducted studies on anti-smoke interference imaging from the spatial distribution [6][7][8] , polarization properties [9][10][11] , and temporal distribution [12,13] of scattered photons. Currently, several studies have investigated three-dimensional single-photon imaging through different types of atmospheric obscurants, including defogging based on optical system design [14][15][16] , photon-efficient imaging algorithms [17][18][19] , and establishment of smoke scattering models [20][21][22][23][24][25][26][27][28][29] . However, an increase in smoke attenuation coefficient can lead to an increase in the number of scattered photons, causing a mixture of target reflected photons and scattered photons, which reduces the signal-to-noise ratio of the target.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Edge-guided image estimation for array Gm-APD Lidar in smoky environment using multi-scale depth images

Zhang,

Li,

Sun

et al. 2023

6th Optics Young Scientist Summit (OYSS 2023)

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Limited statistical frame number and strong backscatter interference from smoke result in a photon-starved regime, severely limiting the depth imaging capability of array Gm-APD lidar in smoky environment. Here, we propose a depth image estimation algorithm that can significantly improve the integrity of targets in dense smoke environments when signal photons are starved. At the signal level, the algorithm improves the accuracy of extracting signal photons by constructing multi-scale superpixels. At the image level, using edge information of depth images at different scales to guide and fill the original scale depth image achieves efficient noise reduction and improves target integrity. It has been successfully demonstrated under different attenuation lengths and statistical frame numbers. Compared with other state-of-the-art methods, the proposed algorithm has maximum target recovery and structural similarity, especially for the attenuation length (AL) is 3.6 and statistical frame number is 1500 (imaging time is 75ms). This study is of great significance for the fast depth imaging of dynamic targets by array Gm-APD lidar in dense smoke environments.

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Three-dimensional imaging of ships in the foggy environment using a single-photon detector array

Zhang

Sun

et al. 2023

Optik

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Dual-parameter estimation algorithm for Gm-APD Lidar depth imaging through smoke

Cited by 13 publications

References 22 publications

Estimation of smoke echo signal using single-photon lidar

Estimation of smoke echo signal using single-photon lidar

Edge-guided image estimation for array Gm-APD Lidar in smoky environment using multi-scale depth images

Three-dimensional imaging of ships in the foggy environment using a single-photon detector array

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