Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope

Designing new optical systems from the ground up for microscopy imaging tasks such as phase retrieval, requires substantial scientific expertise and creativity. To augment the traditional design process, we propose differentiable microscopy ($\partial\mu$), which introduces a top-down design approach. Using all optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through $\partial\mu$. Furthermore, we conduct comprehensive comparisons with existing all-optical phase retrieval methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new phase microscopy systems and may be extended to other similar applications in optical design.