提出基于聚焦堆栈单体数据子集架构的全局成像方法,该方法可以提高单体内部的重建精度,减少由物体边界深度跳跃带来的重建误差。首先采用Alpha Matting算法获取物体的边界信息,按照物体的边界信息在(x,y)空间中对聚焦堆栈进行精确划分,在深度方向上对聚焦堆栈数据进行筛选,获得聚焦堆栈单体数据子集。然后根据聚焦测度对单体数据子集进行深度重建和全聚焦成像,利用全变差正则化优化计算结果。最后将优化后的聚焦堆栈单体数据子集的重建结果进行全局融合,得到全局场景的深度图和全聚焦图。实验结果表明,提出的基于聚焦堆栈单体数据子集架构的全局成像方法可以提高计算效率和重建结果的质量,为聚焦堆栈计算成像提供一种优化方案。

A global imaging method based on the focus stack monomer data subset architecture is proposed, which it can improve the reconstruction accuracy inside the monomer and reduce the reconstruction error caused by the depth jump of the object boundary. First, the Alpha Matting algorithm is used to obtain the boundary information of the object, the focus stack is accurately divided in the (x, y) space according to the boundary information of the object, and the focus stack data is selected in the depth direction to obtain the focus stack monomer subset. Then, according to the focus measure, depth reconstruction and all-focus imaging are performed on the monomer subset, and the calculation result is optimized by the total variation regularization. Finally, the optimized reconstruction results of the monomer subset of the focus stack are globally fused to obtain the depth map and the full focus map of the global scene. Experimental results show that the proposed global imaging based on the focus stack monomer data subset architecture can improve the computational efficiency and the quality of the reconstruction results, and provide an optimized solution for the focus stack computational imaging.