Point object detection using a NL-means type filter
Laure Genin, Frédéric Champagnat, Guy Le Besnerais, Laurent Coret · 2011
A new algorithm is proposed to detect small objects by background suppression. It relies on a modified version of the non-local means filter introduced by Buades et al. for background prediction. Background pixels are estimated by a weighted average depending on the similarity between neighborhoods pixels. For background suppression, the similarity criterion is modified to be less sensitive to point object presence. The resulting D-NLM filter better detects dim point objects than current detection filters. A straightforward spatio-temporal extension is also proposed, it has the advantage of not requiring prior accurate background motion compensation. Our method is well adapted to slow object velocity and large background motion contexts, where it outperforms significantly conventional spatio-temporal filters.