Image binarization is used to transform a digital image such as a grayscale image into a binary image. Binary images in which pixels are represented by single bits are considerably more compact than grayscale images that may employ eight or more bits to represent each pixel. As such, binary images are attractive for applications that are one or both of memory limited and bandwidth limited. Examples of such applications include, but are not limited to, facsimile (FAX) transmission and certain document types of document scanning.
In general, image binarization may be divided into simple or low complexity methods and complex methods. High complexity methods are often too computationally costly for many applications. As such, many applications essentially require or are dependent upon the use of low complexity methods.
Simple or low complexity image binarization methodologies include global thresholding, error diffusion and various other halftoning techniques. For example, global thresholding may employ an arbitrarily chosen, fixed threshold to map pixel(s) in the grayscale image into a corresponding pixel of the binary image. Alternatively, global thresholding may employ an adaptive global threshold that is based on certain image statistics or on an analysis of a shape or other characteristics of an image histogram, for example. The adaptive global threshold essentially automates selection of the fixed threshold.
Unfortunately, many of the low complexity image binarization methods are targeted at producing good binary representations of either text/line content or natural scene content, but not both. These methodologies often prove insufficient for binarization of images with mixed content (e.g., text/lines and natural scenes). For example, a global threshold may be chosen that provides good natural scene representation but fails to render crisp lines and text in the binary image. On the other hand, image binarization methods that attempt to address mixed content images often either produce uniformly poor binary images or are overly complex and not well suited for many applications.
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