Vortex Coherence Filtering for Enhanced Single‐Photon LiDAR Imaging Through Dense Fog

Light detection and ranging (LiDAR) is an important active imaging modality for adverse‐weather perception, yet in dense fog its detection range and measurement accuracy are severely degraded by the combined effects of backscattering and absorption. Existing fog‐penetrating LiDAR approaches, such as single‐photon detection, time gating, and deep learning, remain limited by strong noise sensitivity, incomplete full‐time‐domain noise suppression, and the inability to improve the raw signal‐to‐noise ratio. Here, vortex coherence filtering (VCF) is introduced into a coaxial scanned single‐photon LiDAR architecture for 3D imaging in dense fog. The method enables fog‐penetrating imaging under dense‐fog conditions with visibility as low as 0.61 m and improves the signal‐to‐background ratio by 37.56 times. 3D reconstruction of targets with different surface types is also demonstrated, and the method is shown to be compatible with existing noise‐suppression strategies, enabling further improvement of the signal‐to‐noise ratio. These results show that VCF can serve as an effective optical front‐end noise‐suppression strategy for improving dense‐fog single‐photon imaging performance, and provide a feasible route toward active imaging and environmental perception under adverse weather conditions.