Image binarization using block boundary pixels mean

Abstract

Input image pixels classification into two intensities like black and white as foreground and background respectively refers to image binarization. Binarization with a local threshold value is termed as adaptive image binarization. Locally adaptive thresholding techniques are local pixels dependent process. Local environment dependent process is normally time consuming one and hence its computational time complexity is also local region dependent. Adaptation depends on the local region contrast condition. Low contrast region may not be adapted correctly. This article presents a new locally adaptive fast binarization technique applied on contrast stretched domain of an input image. The local adaptation is based on the mean of the 9 local block boundary pixels. As it associates only 9 pixels for mean calculation, its computational time complexity is free from local reason block size. As it applies on contrast stretched domain image, it can adapt low contrast region for binarization while other techniques fail. Not only its low contrast adapting capability, it has a local block size free computational time complexity. Hence it is more convenient to adapt the low contrast region very fast as compared with other techniques. From the experimental results, it is observed that it yields fast better result than other related local techniques.