Starshynov, I. , Weimar, M., Rachbauer, L., Hackl, G., Faccio, D. , Rotter, S. and Bouchet, D. (2025) Model-free estimation of the Cramer-Rao bound for deep-learning imaging in complex media. [Data Collection]

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Collection description

The provided code is designed for benchmarking the performance of artificial neural networks (ANNs) for estimating the position of a target hidden behind a dynamic scattering medium, such as a suspension of TiO₂ nanoparticles in glycerol. These code performs Fisher Information (FI) estimation and image processing to assess the precision limits of the ANN models and to compare them to the Cramér-Rao bound (CRB), which sets the ultimate limit for precision in estimation. The goal is to determine whether deep-learning imaging systems can approach this theoretical limit when applied to complex media.

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