Fractional-Order Total Variation Geiger-Mode Avalanche Photodiode Lidar Range-Image Denoising Algorithm Based on Spatial Kernel Function and Range Kernel Function

• Published in: Fractal and fractional Vol. 7; no. 9; p. 674
• Main Authors: Wei, Xuyang, Wang, Chunyang, Xie, Da, Yuan, Kai, Liu, Xuelian, Wang, Zihao, Wang, Xinjian, Huang, Tingsheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Algorithms; Avalanche diodes; Background noise; Differential equations; FOTV; GM-APD lidar; Kernel functions; kernel of the range; Lidar; Monte Carlo method; Monte Carlo simulation; Neighborhoods; Noise reduction; Operators (mathematics); Optical radar; Partial differential equations; Photodiodes; Radar imaging; Radar range; range-image denoising; Remote sensing; Signal to noise ratio; spatial kernel function
• ISSN: 2504-3110; 2504-3110
• DOI: 10.3390/fractalfract7090674

A Geiger-mode avalanche photodiode (GM-APD) laser radar range image has much noise when the signal-to-background ratios (SBRs) are low, making it difficult to recover the real target scene. In this paper, based on the GM-APD lidar denoising model of fractional-order total variation (FOTV), the spatial relationship and similarity relationship between pixels are obtained by using a spatial kernel function and range kernel function to optimize the fractional differential operator, and a new FOTV GM-APD lidar range-image denoising algorithm is designed. The lost information and range anomalous noise are suppressed while the target details and contour information are preserved. The Monte Carlo simulation and experimental results show that, under the same SBRs and statistical frame number, the proposed algorithm improves the target restoration degree by at least 5.11% and the peak signal-to-noise ratio (PSNR) by at least 24.6%. The proposed approach can accomplish the denoising of GM-APD lidar range images when SBRs are low.