Edge detection with image enhancement via dynamic programming

Abstract

A dynamic programming technique is proposed for locally finding edges in gray level digital images. A class of characteristic functions is proposed from which the best “edge function” (according to an optimality criterion) is chosen for each window by the dynamic programming technique. The windows are selected by first decomposing the entire image into equal square regions and then breaking into four equal subsquares any region where the quality of fit of the best edge function (as measured by the optimality criterion) falls below a fixed tolerance, and so on. The procedure is summarized in a conceptual algorithm, and the technique is first illustrated by application to a digital image of an artificial design. Second, for comparison, the dynamic programming method and a split-and-merge method are used for image enhancement on a noisy version of a muscle cell culture image. Then the dynamic programming method and a thresholded gradient operator technique are used for edge detection on both the original and the noisy version of this image.