Deep learning-based color holographic microscopy
This work addresses the need for improved throughput in coherent microscopy systems, particularly for point-of-care histopathology, by enabling accurate color imaging from a single hologram.
The authors tackled the problem of high-fidelity color image reconstruction from a single hologram using a GAN-based framework, achieving accurate color transformation and artifact elimination, as demonstrated on lung and prostate tissue sections with histological stains.
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.