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XLuminA: An Auto-differentiating Discovery Framework for Super-Resolution Microscopy
preprintposted on 2023-10-14, 16:00 authored by Carla Rodríguez, Sören Arlt, Leonhard Möckl, Mario Krenn
Driven by human ingenuity and creativity, the discovery of super-resolution techniques, which circumvent the classical diffraction limit of light, represent a leap in optical microscopy. However, the vast space encompassing all possible experimental configurations suggests that some powerful concepts and techniques might have not been discovered yet, and might never be with a human-driven direct design approach. Thus, AI-based exploration techniques could provide enormous benefit, by exploring this space in a fast, unbiased way. We introduce XLuminA, an original computational framework written in JAX, which offers enhanced computational speed enabled by its accelerated linear algebra compiler (XLA), just-in-time compilation, and its seamlessly integrated automatic vectorization, auto-differentiation capabilities and GPU compatibility. Remarkably, XLuminA demonstrates a computational speed-up factor of x80 with respect to well-established light propagation algorithms. We showcase XLuminA's potential by re-discovering three foundational experiments in advanced microscopy. Ultimately, XLuminA identified a novel experimental blueprint featuring sub-diffraction imaging capabilities.