在计算鬼成像(Computational ghost imaging，CGI)系统中，可以通过估计重构图像的模糊程度获取目标的轴向深度。但该方法易受到背景噪声干扰，且要求像质评价函数有较长的工作距离，导致所需的采样次数较高，限制其实用性。针对这一问题，提出了一种基于自适应对焦窗口的目标深度估计方法。首先根据评价函数的整体特征划分搜索区间，然后在给定区域内对目标实际轴向深度进行迭代搜索。在迭代过程中，通过设计自适应窗口，有效减少背景区域的同时，也保证了窗口内目标的完整性。实验结果表明：该方法大幅降低了评价函数所需的必要工作距离，使其在欠采样条件下同样适用，也减小了背景噪声对评价函数的影响，增强了算法的鲁棒性，进一步完善了基于计算鬼成像系统的深度估计方法。

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.