An Improved Adaptive Binarization Algorithm Based on Fuzzy Logic

Image binarization is divided into global algorithm and local algorithm. Global binarization algorithms have a problem to describe objects that have similar brightness with a single threshold. Local binarization algorithms make boundary lines because these algorithms split the image into a specific size of blocks. Therefore, in this paper, we propose a binarization method to complement these problems. The proposed method uses triangular fuzzy membership function to classify the image into obvious regions and ambiguous regions. Obvious regions are binarized by using global binarization algorithm. Whereas ambiguous regions are binarized by using improved local algorithm. Experimental results show the proposed method binarizes the image with less information loss. Moreover, binarized image describes the object in more detail than global binarization methods and more natural than local binarization method.