Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target

Abstract

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360° complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods.

Hybrid regularization method for LRT image reconstruction under incomplete projections

“…In this section, the LRT projection data is firstly simulated and experimented to validate the effectiveness of the proposed method. The reconstructed results are compared to other typical methods, i.e., ART [18], TV regularization [18,[39][40][41] and L2 regularization [35][36], and the results are evaluated by several measure metrics. Then, the LRT outfield experiment is implemented and the real projection data is utilized for image reconstruction to verify the effect of the proposed method further.…”

“…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]:…”

“…With the SSIM and PSNR measurements, the effectiveness of the proposed method is verified by taking the simulated original image as reference. In real experiment, due to the lack of the original reference image, self-reference metric measurements are usually used as the information entropy (IE) [40,55] and the correlation coefficient (CC) [54,55]. IE of the image represents the richness of the image information.…”

“…However, the TV regularization only considers the local characteristics of the image and does not incorporate the good structural information of the image itself, resulting in poor overall image integrity. Therefore, to resolve the above issues, nonlocal total variation (NLTV) regularization method was proposed which gives attention to both the nonlocal structural similarity of the image and the prior constraint of TV regularization [40][41][42]. Compared to TV regularization, NLTV can better restore the structural characteristics of the target [43].…”

Hybrid regularization method for LRT image reconstruction under incomplete projections

“…However, TV regularization only considers the local characteristics of the image and does not incorporate the good structural information of the image itself, resulting in poor overall image integrity. Therefore, to resolve the above issues, the nonlocal total variation (NLTV) regularization method was proposed which gives attention to both the nonlocal structural similarity of the image and the prior constraint of TV regularization [40][41][42]. Compared to TV regularization, NLTV can better restore the structural characteristics of the target [43].…”