A nonlinear laplace operator as edge detector in noisy images

An edge detection scheme is developed robust enough to perform well over a wide range of signal-to-noise ratios. It is based upon the detection of zero crossings in the output image of a nonlinear Laplace filter. Specific characterizations of the nonlinear Laplacian are its adaptive orientation to the direction of the gradient and its inherent masks which permit the development of approximately circular (isotropic) filters. We have investigated the relation between the locally optimal filter parameters, smoothing size, and filter size, and the SNR of the image to be processed. A quantitative evaluation shows that our edge detector performs at least as well—and in most cases much better—than edge detectors. At very low signal-to-noise ratios, our edge detector is superior to all others tested.