# Super-Resolution Reconstruction of an Array Lidar Range Profile
## 1 Introduction  
A three-dimensional range profile can provide key information such as the precise position, geometry, size and shape of the target, which is promising for a wide range of applications in the fields of target detection, urban security, and autonomous driving [1]. The Geiger Mode Avalanche Photon Diode (GM-APD) array-based lidar used in this paper has the advantages of high sensitivity, long detection range, small size, fast response speed, and the ability to simultaneously acquire the distance and intensity information of the target [2, 3]. At present, foreign published research has shown that the number of array lidar arrays based on GM-APD has reached 256 \(\times\) 256, but in the domestic research on its late start, subject to the device limitations, the number of array pixels is only up to 64 \(\times\) 64 [4]. The subsequent detection task, the distance into the image, contains fewertarget information pixels; feature extraction cannot be performed due to the lack of rich target details and edge information, resulting in the acquisition of target details of the edge with limited information about the features, which reduces the accuracy of the detection and recognition [5]. In this paper, a super-resolution reconstruction method is utilized to improve the range profile resolution and enrich the target details and edge information. The currently existing super-resolution reconstruction methods are mainly categorized into three types: interpolation-based, learning-based, and reconstruction model-based [6; 7]. The widely used interpolation-based super-resolution reconstruction techniques are nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation. Nearest neighbor interpolation [8] considers the unknown image element to have the same distance value as the nearest neighbor image element, which is based on the principle of taking the distance value of the neighboring point with the shortest Euclidean distance among the four neighboring pixel points around the point to be interpolated as the distance value of that point. The bilinear interpolation [9] principle takes the original range profile with its neighboring four points in the image element distance value of the horizontal and vertical direction of the linear interpolation so as to calculate the distance value of the points to be sampled. Bicubic interpolation [10] takes into account not only the effect of the distances of the four directly neighboring points, but also the effect of the rate of change in the distance values between each neighboring point, which better preserves the details and structures in the range profile. However, the interpolation-based method only relies on its own image information, which makes it difficult to recover detail information effectively. Learning-based super-resolution reconstruction techniques mainly use deep learning models to learn the mapping relationship between low-resolution images and high-resolution images and generate high-quality reconstruction results. [PERSON] et al. [11; 12], for the first time combining a convolutional neural network and super-resolution image reconstruction technology, proposed the SRCNN (super-resolution convolutional neural network) method, through a large amount of convolution of the input low-resolution image feature extraction and its reconstruction effect, and its reconstruction efficiency was higher than that of previous image reconstruction methods. Subsequently, [PERSON] [13] et al. proposed the FSRCNN (fast super-resolution convolutional neural network) method on the basis of the SRCNN method to address the problems of insufficient details in processing images, high network computation, and low network computation rate. Learning-based super-resolution reconstruction techniques often require a large number of high-resolution images to establish a sample library in order to conduct super-resolution reconstruction. Deep learning methods rely on large amounts of labeled data for training and excel in handling complex image details and textures.