  Fourier ptychographic microscopy (FPM) is a computational approach geared
towards creating high-resolution and large field-of-view images without
mechanical scanning. To acquire color images of histology slides, it often
requires sequential acquisitions with red, green, and blue illuminations. The
color reconstructions often suffer from coherent artifacts that are not
presented in regular incoherent microscopy images. As a result, it remains a
challenge to employ FPM for digital pathology applications, where resolution
and color accuracy are of critical importance. Here we report a deep learning
approach for performing unsupervised image-to-image translation of FPM
reconstructions. A cycle-consistent adversarial network with multiscale
structure similarity loss is trained to perform virtual brightfield and
fluorescence staining of the recovered FPM images. In the training stage, we
feed the network with two sets of unpaired images: 1) monochromatic FPM
recovery, and 2) color or fluorescence images captured using a regular
microscope. In the inference stage, the network takes the FPM input and outputs
a virtually stained image with reduced coherent artifacts and improved image
quality. We test the approach on various samples with different staining
protocols. High-quality color and fluorescence reconstructions validate its
effectiveness.
