Depth estimation in computational ghost imaging system using auto-focusing method with adaptive focus window

In a Computational Ghost Imaging (CGI) system, the axial depth of the target can be obtained by estimating the degree of blur of the reconstructed image. However, this method is easy to be affected by background noise and requires a long working distance for the image quality evaluation function, so this method needs more samplings and the practicability is reduced. To solve this problem, a target depth estimated algorithm with adapted focusing window was proposed. Firstly the local search interval was divided according to the global characteristics of the evaluation function, and then the actual axial depth of the target was searched iteratively in a given region. In iterations, the use of adaptive window decreased the area of background and contained the whole target. Experiments show that the proposed method greatly reduces the necessary working distance, increases the robustness of this method, reduces the effect of background noise on the evaluation function, and achieves the depth of target under low samplings. This work promotes the development of depth estimation method based on computational ghost imaging system.