Fig. 1: General framework of the machine learning (ML) assisted antibunching SRM.
From: Machine learning assisted quantum super-resolution microscopy
Antibunching-based SRM image acquisition starts with an area of n by m pixels (a) and measures complete antibunching histograms via Hanbury-Brown-Twiss (HBT) interferometry at each pixel (b). The Levenberg-Marquardt (L-M) fit is done on each pixel’s HBT histogram to retrieve \({g}^{\left(2\right)}\left(x,y,0\right)\) distribution. Finally, the super-resolved image is constructed using Eq. 2 (d). Alternatively, ML-assisted approach relies on pre-trained CNN regression model, which allows to accurately predict \({g}^{\left(2\right)}\left(x,y,0\right)\) maps utilizing sparse HBT measurement data (c). The developed approach ensures at least 12 times speed-up compared with the conventional L-M fitting based antibunching SRM.
