Algorithm design for image processing in the context of cellular logic

摘要

Two novel image processing algorithms, developed within the referential framework of cellular logic neighbourhood operations, are presented. The first utilizes grey-level morphological operations to provide varying degrees of image sharpening and blurring. This is achieved by mixing the original image, the expanded image and the shrunk image under the control of a single parameter which alters the proportions of original and modified images which are mixed. The second algorithm permits optimal segmentation of an image containing more than two distinct populations of pixels, by manipulation of the grey-level histogram of the image as an image in its own right. This is implemented by morphological smoothing of the histogram image, followed by detection of minima which are uniquely defined in terms of three neighbourhood operators. Results are presented which illustrate how a result visually close to the original can be retrieved from a deliberately blurred image using the first algorithm, whilst a series of results obtained using the second algorithm show that it works well for images in which the pixel populations are well-separated, but poorly if the populations are too small to survive the histogram smoothing step.