We report observations of extreme events (or dissipative optical rogue waves) in a laser with a modulated parameter (cavity losses). Experimental data supporting the hypothesis that these events are related with multi-stability and external crises is presented. It is also shown that the time separation between a pulse and an extreme event can be predicted more accurately than that between pulses of average intensity, in agreement with the theoretical description and opening the road to the prediction and control of extreme optical events.
We present
gSUPPOSe, a novel, to the best
of our knowledge, gradient-based implementation of the SUPPOSe
algorithm that we have developed for the localization of single
emitters. We study the performance of gSUPPOSe and compressed sensing
STORM (CS-STORM) on
simulations of single-molecule localization microscopy (SMLM) images
at different fluorophore densities and in a wide range of
signal-to-noise ratio conditions. We also study the combination of
these methods with prior image denoising by means of a deep
convolutional network. Our results show that gSUPPOSe can address the
localization of multiple overlapping emitters even at a low number of
acquired photons, outperforming CS-STORM in our quantitative analysis
and having better computational times. We also demonstrate that image
denoising greatly improves CS-STORM, showing the potential of deep
learning enhanced localization on existing SMLM algorithms. The
software developed in this work is available as open source Python
libraries.
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