We developed a physics-informed deep neural network architecture able to achieve signal-to-noise ratio improvements starting from low-exposure noisy data. Our model is based on the nature of the photon detection process characterized by a Poisson probability distribution, an information which we included in the training loss function. Our approach surpasses previous algorithm performance for microscopy images; moreover, the generality of the physical concepts employed here, makes it readily exportable to any imaging context.