Detection of Step Edges in Noisy One-Dimensional Data

A method of detecting step edges in noisy one-dimensional input data is described. The method involves examination of differences in average gray level over ranges of positions and sizes. Unlike previously described methods, it remains reliable when edges occur close to one another.