We investigate a systematic improvement for 3D range-gated imaging in scattering environments. Drawbacks including absorption, ambient light, and scattering effect are studied. The former two are compensated through parameter estimation and preprocessing. With regard to the scattering effect, we propose a new 3D reconfiguration algorithm using a Bayesian approach that incorporates spatial constraints through a general Gaussian Markov random field. The model takes both scene depth and albedo into account, which provides a more informative and accurate restoration result. Hyper-parameters for the statistical mechanism are evaluated adaptively in the procedure and an iterated conditional mode optimization algorithm is employed to find an optimum solution. The performance of our method was assessed via conducting various experiments and the results also indicate that the proposed method is helpful for restoring the 2D image of a scene with improved visibility. (C) 2020 Optical Society of America
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