  Fourier ptychography has attracted a wide range of focus for its ability of
large space-bandwidth-produce, and quantative phase measurement. It is a
typical computational imaging technique which refers to optimizing both the
imaging hardware and reconstruction algorithms simultaneously. The data
redundancy and inverse problem algorithms are the sources of FPM's excellent
performance. But at the same time, this large amount of data processing and
complex algorithms also greatly reduce the imaging speed. In this article, we
propose a parallel Fourier ptychography reconstruction framework consisting of
three levels of parallel computing parts and implemented it with both central
processing unit (CPU) and compute unified device architecture (CUDA) platform.
In the conventional FPM reconstruction framework, the sample image is divided
into multiple sub-regions for separately processing because the illumination
angles for different subregions are varied for the same LED and different
subregions contain different defocus distances due to the non-planar
distribution or non-ideal posture of biological sample. We first build a
parallel computing sub-framework in spatial domain based on the above-mentioned
characteristics. And then, by utilizing the sequential characteristics of
different spectrum regions to update, a parallel computing sub-framework in the
spectrum domain is carried out in our scheme. The feasibility of the proposed
parallel FPM reconstruction framework is verified with different experimental
results acquired with the system we built.
