Precise line scratch detection with a contrario methods

1. Preciseline scratch detection with a contrariomethods Alasdair NEWSON, Patrick PEREZ, Andrés ALMANSA, Yann GOUSSEAU Prior Art Context • Kokaram (1996): • First line scratch model • Bayesian estimation of scratch parameters • Joyeuxet al. (1999) : • Temporal aspects included • Scratch tracking with Kalman filter • Bruniet al. (2004) : • Generalised model for scratches • Old archives deteriorate : restoration necessary • Manual restoration extremely time-consuming • Line scratches : persistent defects (last several frames) • Robust detection difficult • Characteristics difficult to determine • Easily confused with thin vertical structures Gray Level Pixel/median value difference Column Left average Right average Scratch width Zoom Scratch example Scratch Pixel Grouping Pixel-wise Scratch Detection Results A contrario methods : object “meaningful” if it is unlikely to be produced in noise image “From Gestalt Theory to Image Analysis : a Probabilistic Approach”, A. Desolneux, L. Moisan, J-M. Morel, Springer-Verlag, 2008 Segment probability in noise follows a Binomial law Original image Detected segments Recall/precision comparison with Bruni’s algorithm (in percentage) • Median difference threshold • Left/right neighbourhood coherence 1/ Accept segments with low Number of False Alarms (NFA) 2/ Impose maximality principle “Segment neither contains nor is contained by a better segment” 3/ Impose exclusion principle Original image Binary detection map • Pros • Sensitive, precise detection • Robust to noise and texture • Good recall • Cons • Over-detection • Thin vertical structures Question : how to group the detected points ? “Associate a pixel to the best segment only”