A U-Net trained on Poisson-noisy images reconstructs parameterized sinusoidal patterns with mean-squared error close to the quantum Cramér-Rao bound, but the claim that it is the optimal allowed estimator is not supported by the variance-only bound.
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Imaging at the quantum limit with convolutional neural networks
A U-Net trained on Poisson-noisy images reconstructs parameterized sinusoidal patterns with mean-squared error close to the quantum Cramér-Rao bound, but the claim that it is the optimal allowed estimator is not supported by the variance-only bound.