A Fast ModelBased Restoration of Colour Movie Scratches 
Michal Haindl Jiri Filip 
This work presents a new type of scratch removal algorithm based on a causal adaptive multidimensional multitemporal prediction. The predictor use available information from the neighbourhood of a missing multispectral pixel due to spectral, temporal and spatial correlation of video data but not any information from the failed pixel itself. The model assumes white Gaussian noise in each spectral layer, but layers can be mutually correlated. A significant improvement of the 3D model performance is obtained if the temporal information is included, i.e., using the 3.5D causal AR model. Such information is natural to obtain from previous or/and following frame(s) for which we know all necessary data, due to high betweenframe temporal correlation. Thanks to this we can treat data from different frames (specified by the contextual neighbourhood) in the same way, so we attach to each data information about its shift according to predicted pixel placement. The contextual neighbourhood has to be causal (in the reconstructed frame lattice subspace) . It means that the predictor can use only data from the model history. Then if we assume normalWishart parameter prior the predictor have analytical (not iterative) solution.
Original  The scratch 
Linear interpolation 
3D CAR model 
3.5D CAR model without motion compensation 
3.5D CAR model with motion compensation 
Method comparison with classical restoration methods.
The scratch 
Averaging  Median filtering 
Linear interpolation 
3D CAR model 
3.5D CAR model 

The scratch 
Averaging  Median filtering 
Linear interpolation 
3D CAR model 
3.5D CAR model 

Back to the demonstration page. 