A New Class of Detail-Preserving Filters for Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Picture Restoration: Digital Algorithms for Artefact Suppression in Degraded Motion Picture Film and Video
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Deterioration detection for digital film restoration
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
LUM filters: a class of rank-order-based filters for smoothing andsharpening
IEEE Transactions on Signal Processing
Multistage order statistic filters for image sequence processing
IEEE Transactions on Signal Processing
IEEE Transactions on Circuits and Systems for Video Technology
A fusion-based approach to digital movie restoration
Pattern Recognition
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This paper proposes a novel method based on Dempster-Shafer (belief function) theory for the detection of blotches in digitized archive film sequences. The detection scheme relies on the fusion of two uncorrelated blotch detectors, one working in the spatial domain and the other one in the temporal domain. The imprecision and uncertainty of both detectors have been modeled using belief function theory, and their combination improves the decision, by taking into account the ignorance and the conflict between detectors. Quantitative evaluation using real blotches ground truth shows that this combination scheme improves the global performance, and compares favorably with two classical blotch detectors.