Research of the Richardson-Lucy Algorithm Based Deblurring Method for Enhancing Spatial Resolution in Streak-Tube Imaging Lidar

The streak tube imaging lidar(STIL) is characterized by a wide-field array imaging capability and high temporal precision. The two-dimensional streak images it acquires can record the spatial characteristics of the target echoes. However, in actual airborne ground detection, the collected streak images often exhibit blurring phenomena, which reduces the system's spatial resolution. To address this issue, we applied the Richardson-Lucy(RL) deconvolution algorithm to process the streak images, optimizing and validating the deconvolution point spread function(PSF) parameters. Based on flight test data, the RL algorithm effectively reduces blurring in streak images, significantly enhancing the spatial resolution of range images captured by the airborne streak-tube imaging lidar. This improvement further provides strong support for subsequent tasks such as point cloud generation, target recognition, and classification.