  We report a framework based on a generative adversarial network (GAN) that
performs high-fidelity color image reconstruction using a single hologram of a
sample that is illuminated simultaneously by light at three different
wavelengths. The trained network learns to eliminate missing-phase-related
artifacts, and generates an accurate color transformation for the reconstructed
image. Our framework is experimentally demonstrated using lung and prostate
tissue sections that are labeled with different histological stains. This
framework is envisaged to be applicable to point-of-care histopathology, and
presents a significant improvement in the throughput of coherent microscopy
systems given that only a single hologram of the specimen is required for
accurate color imaging.
