Motion blur suppression method for time-of-flight imaging systems based on differential correlation sampling data

Time-of-Flight (ToF) imaging systems, capable of their high frame rate, high resolution, and cost-effectiveness, enable diverse applications. However, their ranging performance is significantly degraded by motion blur caused by relative motion between the system and the scene. To address this challenge, this paper proposes a motion blur suppression method based on differential correlation sampling (DCS) data for time-of-flight imaging systems, which employs a three-step strategy: firstly, adaptive thresholds for motion blur detection are established based on noise levels to identify blurred regions; secondly, the occurrence time of motion blur is determined, and compensation is performed using the complementary properties of DCS data to suppress motion blur; finally, an enhanced bilateral filtering is applied according to the spatial distribution characteristics of motion-blurred regions to further improve suppression efficacy. Experimental validation in both laboratory and real-world environments demonstrates the superiority of the proposed method. Compared with existing techniques, our approach significantly reduces the root mean squared error (RMSE) and enhances metrics such as the noise reduction ratio, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). This study offers a novel framework for suppressing motion blur in ToF imaging systems and provides valuable insights into understanding motion blur in three-dimensional measurement systems.