A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images

High-quality image restoration is typically challenging due to low signal–to–background ratios (SBRs) and limited statistics frames. To address these challenges, this paper devised a method based on fractional-order total variation (FOTV) regularization for recovering Geiger-mode avalanche photodiode (GM-APD) light detection ranging (lidar) depth images. First, the spatial differential peak-picking was used extract target from SBR FOTV introduced recovery model, which incorporates operator, in order realize FOTV-regularization-based recovery. These frameworks were establish an algorithm GM-APD FOTV. The simulation experimental results demonstrate that FOTV-recovery improved reduction degree, peak signal–to–noise ratio, structural similarity index measurement by 76.6%, 3.5%, 6.9% more than TV, respectively, same statistic frame conditions. Thus, approach able effectively recover lidar images