A Fast Model-Based Restoration of Colour Movie Scratches

Abstract: 
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 between-frame 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 normal-Wishart parameter prior the predictor have analytical (not iterative) solution.

Scratch restoration algorithm - 4 identical 3.5D causal auroregressive models.


Results

3D CAR model - known data from the actual frame only
3.5D CAR model - known data from the actual and the previous frame

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


 

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