# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 3 Experimental Results
### Dataset Experiment and Result Analysis
The reconstruction of range profile details based on the traditional POCS method is better than that of the interpolation method, but the edges are still fuzzy when combined with the local magnification of Figure 3d. In Figure 3e, using the statistical characteristics of the edge intensity obtained by the Scharr operator to adjust the size of the correction threshold, the effect of the reconstructed range profile at the edge is better than POCS, but there are still artifacts on the edge. In Figure 3f,  
Figure 4: Super-resolution rendering of the car range profile with **(a)** the original car range profile; **(b)** bilinear car range profile; **(c)** bicubic car range profile; **(d)** POCS car range profile; **(e)** POCS [17] car range profile; **(f)** POCS with the LK car range profile; **(g)** POCS with gradient car range profile; and **(h)** the proposed car range profile. the motion estimation between the low-resolution image sequences is more accurate due to the introduction of the LK optical flow method, and the display effect is clearer than that in Figure 3b-e,g. The edge processing effect needs to be improved, and the target edge contour cannot be effectively distinguished from the background. The gradient constraint is introduced in Figure 3g, and although the edge processing effect is better than that in Figure 3d, the overall detail display effect of the target is worse than that in Figure 3f. In Figure 3h, by introducing the LK optical flow method and gradient constraint, the overall details and edge effects of the target are better than those in Figure 3b-g, which can effectively retain the details and edge information of the target. In order to further verify the effectiveness of the proposed method in image detail edge processing, the vehicle range profile with abundant target surface details and edge information was selected from the dataset for experimental verification. It can be seen from Figure 4 that the range profile reconstructed based on POCS is better than the overall contour details reconstructed by interpolation. Due to the introduction of the adaptive correction factor in the method proposed in the literature [17], Figure 4e is sharper than Figure 4d at the lights and wheels. Compared to Figure 4f, Figure 4h better highlights the difference between the edge and the background at the edges, such as the wheel, but better shows the overall details of the object. The range profile reconstructed by the method proposed in this paper is sharper in edge and image detail than that proposed in the literature [17] and is the closest to the original range profile. It can be seen from Tables 1 and 3 that the POCS super-resolution reconstruction method is superior to the interpolation-based super-resolution reconstruction method in the evaluation indexes of reconstructed images under different magnifications. Our method improves the AG by at least 1.18% and the ES is improved by at least 1.5%, for the sofa range profile reconstruction image quality at different magnifications; for the car range profile at different magnifications, the AG is improved by at least 1.37% and the ES is improved by at least 0.84%. From Tables 2 and 4, we can also see that our algorithm outperforms other comparison algorithms in terms of the PSNR and SSIM.