Gaussian derivative model for edge enhancement

摘要

In this paper we report the result of a set of computer experiments carried out to enhance edges in digital images. We use a special line-weight function, which is a combination of zero- and second-order Hermite functions. We are motivated by the physiological evidence reported in R. A. Young, Spatial Vision2(4), 273–293 (1987), that visual receptive fields in the primate eye are shaped like the sum of a Gaussian function and its Laplacian. This function can also be derived mathematically when the contrast sensitivity experiments in psychophysics are posed as an eigenvalue problem (A. L. Stewart and R. Pinkham, Biological Cybernetics, to appear). We introduce the concept of multi-scale analysis into the line weight function. We have attempted to understand the role played by the weight associated with each term of the proposed function. The experimental results with one- and two-dimensional data show that the proposed function has extremely good localization capability (i.e. the points marked by the operator is as close as possible to the center of the true edge).