Tech Report CS-07-03
Denoising Archival Films using a Learned Bayesian Model
Teodor Mihai Moldovan, Stefan Roth and Michael J. Black
We develop a Bayesian model of digitized archival lms and use this for denoising, or more specifically de-graining, individual frames. In contrast to previous approaches our model uses a learned spatial prior and a unique likelihood term that models the physics that generates the image grain. The spatial prior is represented by a high-order Markov random field based on the recently proposed Field-of-Experts framework. We propose a new model of the image grain in archival films based on an inhomogeneous beta distribution in which the variance is a function of image luminance. We train this noise model for a particular film and perform de-graining using a diffusion method. Quantitative results show improved signal-to-noise ratio relative to the standard ad hoc Gaussian noise model.
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