Bayesian denoising of archival film requires a likelihood model that captures the image noise and a spatial prior that captures the statistics of natural scenes. For the former we learn a statistical model of film noise that varies as a function of image brightness. For the latter we use the recently proposed Field-of-Experts framework to learn a generic image prior that capture the statistics of natural scenes. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits non-linear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. The prior model alone can be used to inpaint missing image structures and the data noise model can be used to simulate realistic film grain. Additionally we demonstrate how the learned likelihood and prior models can be used to denoise archival film footage. Joint work with Stefan Roth and Teodor Moldovan.