Automatic processing and full analysis of in situ liquid phase scanning transmission electron microscopy (LP-STEM) acquisitions are yet to be achievable with available techniques. This is particularly true for the extraction of information related to the nucleation and growth of nanoparticles (NPs) in liquid as several parasitic processes degrade the signal of interest. These degradations hinder the use of classical or state-of-the-art techniques making the understanding of NPs formation difficult to access. In this context, we propose aquaDenoising, a novel simulation-based deep neural framework to address the challenges of denoising LP-STEM images and videos. Trained on synthetic pairs of clean and noisy images obtained from kinematic-model-based simulations, we show that our model is able to achieve a fifteen-fold improvement in the signal-to-noise ratio of videos of gold NPs growing in water. The enhanced data unleash unprecedented possibilities for automatic segmentation and extraction of structures at different scales, from assemblies of objects down to the individual NPs with the same precision as manual segmentation performed by experts, but with higher throughput. The present denoising method can be easily adapted to other nanomaterials imaged in liquid media. All the codes developed in the present work are open and freely available.