  Whole slide imaging (WSI) is an emerging technology for digital pathology.
The process of autofocusing is the main influence of the performance of WSI.
Traditional autofocusing methods either are time-consuming due to repetitive
mechanical motions, or require additional hardware and thus are not compatible
to current WSI systems. In this paper, we propose the concept of
\textit{virtual autofocusing}, which does not rely on mechanical adjustment to
conduct refocusing but instead recovers in-focus images in an offline
learning-based manner. With the initial focal position, we only perform
two-shot imaging, in contrast traditional methods commonly need to conduct as
many as 21 times image shooting in each tile scanning. Considering that the two
captured out-of-focus images retain pieces of partial information about the
underlying in-focus image, we propose a U-Net-inspired deep neural network
based approach for fusing them into a recovered in-focus image. The proposed
scheme is fast in tissue slides scanning, enabling a high-throughput generation
of digital pathology images. Experimental results demonstrate that our scheme
achieves satisfactory refocusing performance.
