Research on infrared image guided GM-APD range image recovery algorithm under limited detections. (July 2023)

• Record Type: Journal Article
• Authors: Wang, Mingqi; Sun, Jianfeng; Li, Sining; Lu, Wei; Zhou, Xin; Zhang, Hailong
• Is Part Of: Optics and lasers in engineering. Volume 166(2023)
• Publication Date: 2023-07
• Subjects: GM-APD -- Range image recovery -- GIR method -- Lidar; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2023.107579
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A guided image recovery (GIR) method is first proposed. The proposed algorithm has high recovery degree, denoising, and fidelity performance with strong noise and fewer detections. This paper provides a new research direction for GM-APD lidar range image reconstruction by referring to infrared images. Abstract: Geiger-mode avalanche photodiode (GM-APD) focal plane array lidar has a high photon-sensitive detection ability and can acquire three-dimensional(3D) distance information. However, high noise intensity and limited detections can easily lead to the loss of target pixels in the range image, and excellent signal extraction and noise suppression technology become crucial. To solve this problem, we propose a guided image recovery (GIR) method which first uses the infrared image information to recover the GM-APD range image. Unlike image fusion technology, which sacrifices the true range value or the objectively existing 3D range image to get the complete image, it cannot only recover the complete image but also guarantee the 3D image is true. The main innovation of the proposed algorithm is to search the missing pixels of the range image based on the boundary information of infrared images and extracts the range values from their own original measurement data according to the adjacent pixels' information. According to Monte Carlo simulation and experimental data analysis, the proposed method has excellent target recovery, noise suppression, and fidelity performance under limited detections and low SBRs conditions, which provides an important reference for GM-APD lidar real-time detection. It also provides a new research direction for GM-APD lidar range image reconstruction.