Single Image Deblurring for Pulsed Laser Range-Gated Imaging System with Multi-Slice Integration

Abstract

The multi-slice integration (MSI) method is one of the approachs to extend the depth of view (DOV) of the pulsed laser range-gated imaging (PLRGI) system. When the DOV is large enough and exceeds the depth of focus of the system, it may make some targets in the image clear and others blurred. In addition, forward scatter is also considered to have a blurring effect on the image. There is very little literature to solve the combined effect of forward scatter and defocus. An imaging model is built based on the model from Jaffe–McGlamery and Fourier optics. According to the imaging model, backscattered light is independent from reflected light from the target, and forward scatter has a relationship with the reflected light. Thus, backscattered light should be removed before deblurring. First, rolling ball and intensity transformation are used to remove the backscattered light and enhance the image. Then, a deep learning model based on Transformer is used to deblur the image. To enable the deep learning model to accommodate different degrees of blurred image, 16 different blur kernels are generated according to the imaging model. Sharp images from a DPDD dataset were chosen to train the model. Images of varying degrees of blur were collected from a water tank and a boat tank by the PLRGI system as test sets. Image deblurring results show that the proposed method can remove different levels of blur and can deal with images which have sharp targets and blurred targets together.