  Falling raindrops are usually considered purely negative factors for
traditional optical imaging because they generate not only rain streaks but
also rain fog, resulting in a decrease in the visual quality of images.
However, this work demonstrates that the image degradation caused by falling
raindrops can be eliminated by the raindrops themselves. The temporal
second-order correlation properties of the photon number fluctuation introduced
by falling raindrops has a remarkable attribute: the rain streak photons and
rain fog photons result in the absence of a stable second-order photon number
correlation, while this stable correlation exists for photons that do not
interact with raindrops. This fundamental difference indicates that the noise
caused by falling raindrops can be eliminated by measuring the second-order
photon number fluctuation correlation in the time domain. The simulation and
experimental results demonstrate that the rain removal effect of this method is
even better than that of deep learning methods when the integration time of
each measurement event is short. This high-efficient quantum rain removal
method can be used independently or integrated into deep learning algorithms to
provide front-end processing and high-quality materials for deep learning.
