Shape from focus (SFF) is a widely used method for recovering the three-dimensional (3D) shape of an object from an image sequence with various focus measure operators. However, most previous studies have focused on evaluating the depth map using a specified focus measure operator based on a single perspective. These methods severely limit the accuracy of the reconstruction results for a complex real scene. In this study, a novel SFF method based on a multiscale fusion perspective is proposed. First, the feasibility of obtaining a mapping relationship between high-frequency coefficients and the depth maps by the stationary wavelet transform (SWT) is discussed. Next, level reduction is introduced to approximate the target depth map using multilevel high-frequency coefficients. Then, an aggregation pulse coupled neural network (a-PCNN) model with variable-size cross sum modified Laplacian (CSML) operators is used as the mapping functions from selected high-frequency coefficients to various window size depth maps. Finally, a hierarchical screening method (HSM) is proposed to yield a more accurate reconstruction result by fusing depth maps with various window sizes. The experimental results demonstrate that the proposed method realizes a more accurate depth map estimation and better surface consistency of the reconstruction results than the compared SFF methods and several advanced multifocus image fusion methods.

聚焦形状（SFF）是一种广泛使用的方法，用于使用各种聚焦度量运算符从图像序列中恢复对象的三维（3D）形状。但是，大多数先前的研究都集中在使用基于单个视角的指定焦点度量运算符来评估深度图。这些方法严重限制了复杂真实场景的重建结果的准确性。在这项研究中，提出了一种基于多尺度融合视角的新型SFF方法。首先，讨论了通过平稳小波变换（SWT）获得高频系数与深度图之间的映射关系的可行性。接下来，引入降级以使用多级高频系数来近似目标深度图。然后，具有可变大小的交叉和修改的拉普拉斯算子（CSML）运算符的聚集脉冲耦合神经网络（a-PCNN）模型被用作从选定的高频系数到各种窗口大小深度图的映射函数。最后，提出了一种分层筛选方法（HSM），通过融合具有各种窗口大小的深度图来产生更准确的重建结果。实验结果表明，与比较的SFF方法和几种先进的多焦点图像融合方法相比，该方法实现了更准确的深度图估计和更好的重建结果表面一致性。提出了一种分层筛选方法（HSM），通过融合具有各种窗口大小的深度图来产生更准确的重建结果。实验结果表明，与比较的SFF方法和几种先进的多焦点图像融合方法相比，该方法实现了更准确的深度图估计和更好的重建结果表面一致性。提出了一种分层筛选方法（HSM），通过融合具有各种窗口大小的深度图来产生更准确的重建结果。实验结果表明，与比较的SFF方法和几种先进的多焦点图像融合方法相比，该方法实现了更准确的深度图估计和更好的重建结果表面一致性。