Binarization of uneven-lighting image by maximizing boundary connectivity

Abstract

By using the constraint of boundary connectivity, a new technique for binarizing a gray level image is proposed. Initially, the gray level image is partitioned into a number of fixed-size subimages and the best binarization of each subimage is estimated. Of all subimages, the one with the best binarization effect is first binarized to be the starting binary area of the gray level image. Next, of the subimages neighboring the binary area, the one with the best binarization effect is selected to perform the following test. If the maximal boundary connectivity achieved by binarizing the selected subimage with a specific threshold satisfies the constraint of boundary connectivity, the subimage will thus be binarized to update the partially-binarized gray level image, otherwise, the subimage will be marked “ignored” and remain gray throughout the subimage binarization procedure. The procedure will be repeated until no further subimages neighboring the binary area can be binarized. If there are any gray subimages without being marked “ignored,” the one with the best binarization effect will be binarized to create a new binary area in the partially-binarized gray level image, and then the procedure of extending the binary area will be repeated again. At the last stage, the proposed method constructs a threshold surface for the original gray level image based on the binarized subimages. Experimental results show the effectiveness of this method in binarizing uneven-lighting gray level images.