Application of Variational Bayesian Deconvolution Method in Laser Reflective Tomography Imaging

Yu, 谷雨 Gu; Yihua, 胡以华 Hu; Shiqi, 郝士琦 Hao; Jin-cheng, Wang; Di, Wang

doi:10.3788/aos201636.0611003

Cited by 2 publications

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“…1, the target is irradiated by a parallel laser beam emitted from the laser transmitter. The reflectance projection 0 ( , ) pr  of the target can be expressed as follows [11,47]:…”

Section: Lrt Projection Modelmentioning

confidence: 99%

Hybrid regularization method for LRT image reconstruction under incomplete projections

Guo,

Zhihan,

Wang

et al. 2024

Preprint

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Lidar reflective tomography (LRT) system is able to achieve long-distance target detection and collect laser projection data for target reconstruction. In target detection with laser, the projection data is often incomplete or missing, where the traditional reconstruction algorithms are no longer effective. Therefore, this paper applies the regularization model to LRT image reconstruction and proposes a hybrid regularization method for higher LRT imaging performance, to prevent the over-fitting problem of the model and improve the speed and stability of the proposed method. Meanwhile, the edge and the structural characteristics of the reconstructed target with LRT is better enhanced by using the proposed hybrid regularization method. By conducting the LRT outfield experiment and using the collected LRT projection data, the validity of the proposed method in incomplete projection reconstruction is well proved.

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“…1, the target is irradiated by a parallel laser beam emitted from the laser transmitter. The reflectance projection 0 ( , ) pr  of the target can be expressed as follows [11,47]:…”

Section: Lrt Projection Modelmentioning

confidence: 99%

Hybrid regularization method for LRT image reconstruction under incomplete projections

Guo,

Zhihan,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…1, the target is irradiated by a parallel laser beam emitted from the laser transmitter. The reflectance projection 0 ( , ) pr  of the target can be expressed as follows [11,47]: When the projections at all irradiation angles are collected, the LRT echo projection data to be processed is obtained. The next step is to align the projection data [48] and convert the 360° reflection projection data 0 ( , ) pr  into the 180° transmission data to reduce the difficulty of image reconstruction [40]:…”

Section: Lrt Projection Modelmentioning

confidence: 99%

Hybrid regularization method for LRT image reconstruction under incomplete projections

Guo,

Zhihan,

Jiang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Application of Variational Bayesian Deconvolution Method in Laser Reflective Tomography Imaging

Cited by 2 publications

References 0 publications

Hybrid regularization method for LRT image reconstruction under incomplete projections

Hybrid regularization method for LRT image reconstruction under incomplete projections

Hybrid regularization method for LRT image reconstruction under incomplete projections

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