Deep learning-based holographic reconstruction for color imaging of pathology slides

We present a deep learning-based, high-throughput, accurate colorization framework for holographic imaging systems. Using a conditional generative adversarial network (GAN), this method can be used to remove the missing-phase-related spatial artifacts using a single hologram. When compared to the absorbance spectrum estimation method, which is the current state-of-the art method used to perform color holographic reconstruction, this framework is able to achieve a similar performance while requiring 4-fold fewer input images and 8-fold less imaging and processing time. The presented method can effectively increase the throughput for color holographic microscopy, providing the possibility for histopathology in resource limited environment.