Efficient detection of temporally impulsive dirt impairments in archived films

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.