Denoising Archival Films using a Learned Bayesian Model

We develop a Bayesian model of digitized archival films 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 signalto-noise ratio relative to the standard ad hoc Gaussian noise model.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Teodor Mihai Moldovan, Stefan Roth, Michael J. Black
• Keywords: Bayesian Model, Digitized Archival Films, Gaussian Noise Model, ICIP 2006, Image Grain, Image Processing, Spatial Prior