Learning to synthesize: Robust phase retrieval at low photon counts

An artifact-free computational approach to extract the phase of light from noisy intensity signals improves imaging of transparent objects, such as biological cells, under low light conditions. Deep neural networks are trained to operate on these two frequency bands, before a final algorithm recombines them into a full-band phase image. This method avoids the tendency of automatic phase extraction programs to over-represent low frequencies. The retrieval of phase of electromagnetic fields is one of the most important problems in optics as it allows the shape of transparent objects, including cells, to be quantified using visible light. Phase is a quantity that relates to the wave nature of light; it is not directly detectable by our eyes or common cameras, and yet carries important information about objects the light went through.