A New Class of Detail-Preserving Filters for Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterioration detection for digital film restoration
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A New Auto-Regressive (AR) Model-Based Algorithm for Motion Picture Restoration
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
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
MCMC for joint noise reduction and missing data treatment indegraded video
IEEE Transactions on Signal Processing
A new efficient approach for the removal of impulse noise from highly corrupted images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach
IEEE Transactions on Image Processing
Efficient multiframe Wiener restoration of blurred and noisy image sequences
IEEE Transactions on Image Processing
Detection of missing data in image sequences
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Fusion of complementary detectors for improving blotch detection in digitized films
Pattern Recognition Letters
Fast and Efficient MRF-Based Detection Algorithm of Missing Data in Degraded Image Sequences
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Archive film restoration based on spatiotemporal random walks
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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We propose a novel approach for the detection of temporally impulsive dirt impairments in archived film sequences. Our method does not require motion compensation and uses raw differences between the current frame and each of the previous and next frames to extract a confidence signal which is used to localize and label dirt regions. A key feature of our method is the removal of false alarms by local region-growing. Unlike other work utilizing manually added dirt impairments, we test our method on real film sequences with objective ground truth obtained by infrared scanning. With confidence information extracted from color channels, dirt areas of low contrast to the corresponding gray image can be successfully detected by our method when motion-based methods fail. Comparisons with established algorithms demonstrate that our method offers more efficient, robust and accurate dirt detection with fewer false alarms for a wide range of test material.