A novel algorithm for edge detection from direction-derived statistics

This paper presents a novel algorithm for edge detection from an image that is corrupted by additive Gaussian noise. The proposed approach uses a criterion for finding pixels where the image gradient is dominated by the noise term. From these pixels, it produces an approximate distribution of the noise gradient magnitude, and then, using this distribution, a threshold for a given false alarm rate is obtained. After grouping the pixels into structured clusters, the cluster average is tested against the threshold to detect the edge. The performance of the proposed method was found to be superior to other automatic thresholding methods tested