A Blooming-Region-Aware Directional Weighting Filter for Correcting Blooming-Induced Background Depth Distortion in AMCW-ToF LiDAR Imaging of a High-Reflectance Object

When measuring a high-reflectance target using time-of-flight LiDAR (ToF LiDAR), strong reflected optical signals are generated. Pixels receiving such strong reflected optical signals become saturated, and intensity and depth distortions occur even around the saturated pixels owing to crosstalk, internal lens scattering, and related effects. Previous studies on depth distortion in ToF cameras and LiDAR caused by such blooming have mainly focused on removing distorted background pixels, which limits their ability to directly correct background depth distortion around high-reflectance targets. In this paper, we propose a blooming-region-aware directional weighting filter method that corrects blooming-induced background depth distortion around a high-reflectance target using only the intensity map and depth map acquired from a single amplitude-modulated continuous-wave time-of-flight (AMCW-ToF) LiDAR. The proposed method detects the blooming-affected background region based on the intensity map and the gradient information of the intensity map. Subsequently, a window kernel is applied to each correction target pixel within the detected region, and the distorted depth of the correction target pixel is corrected through multi-weight depth filtering that considers the directional relation among pixels inside the kernel. In the validation process, the correction performance was evaluated in terms of mean squared error (MSE) and root mean squared error (RMSE) according to changes in the filtering method, target distance, target direction, and background SNR. The validation results demonstrate that the proposed method reduced the RMSE by an average of 49.811% compared with the blooming-distorted depth under various experimental conditions, indicating that it can mitigate background depth distortion around high-reflectance targets.