Denoising Archival Films Using a Field-of-experts Model of Film Grain and Natural Image Statistics

Friday, February 10, 2006 - 10:30am - 11:30am
EE/CS 3-180
Michael Black (Brown University)
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.
MSC Code: