In this work we pose the parameter estimation of the Poisson-Gaussian noise model as a parametric model estimation problem. We first take patches from the image/video to analyze and treat variance stabilization transforms, e.g., the classical Generalized Anscombe transform, as a parametric model, which we fit to the patches using the Hough transform. This algorithm allows to successfully estimate the noise parameters, is computationally efficient, and is fully parallelizable. We present an application to calcium imaging data, where the estimated parameters are used to enhance state-of-the-art processing pipelines.