Nonlocal-means-based smallest univalue segment assimilating nucleus edge detector

Abstract

To improve the antinoise performance of the smallest univalue segment assimilating nucleus (SUSAN) edge detector, a nonlocal means-based SUSAN edge detector is proposed. The proposed method first determines the initial SUSAN edge response based on the image patch convolved with an adaptive kernel instead of the single pixel. Then it computes the final edge response using the weighted sum of the initial edge responses of the pixels with their structures similar to the considered pixel. Extensive simulations on natural and real images demonstrate that compared with state-of-the-art detectors, the proposed method performs much better in terms of robustness to noise and edge detection and it provides significantly higher values of Pratt’s figure of merit and performance measure.