Comparison of four deblurring methods for the streak image of streak tube imaging lidar

In this study, the Richardson–Lucy (R–L) iteration, Tikhonov (T–K) regularization, and fast iterative shrinkage-thresholding algorithm (FISTA) are first used to deblur streak images. The deblur performances of these three methods are compared with that of Wiener deconvolution. The spatial resolutions of Wiener deconvolution, R–L iteration, T–K regularization, and FISTA improved from 9 mm to 4.5, 5, 5, and 5 mm, respectively. The root-mean-square errors (RMSE) of one -, two -, and three-plane targets decreased effectively using these three deblur methods. The R–L iteration performs the best in terms of the RMSE for the two-plane target. The effects of the parameters used in each method are investigated.