TABLE OF CONTENTS Chapter 3 dealt with the class of image processing algorithms that enhance images through pixel intensity remapping via lookup tables that discretize a gray-level transform function T( r). In this chapter we move on to a different but related set of algorithms, broadly classified as image filtering. The topic of filtering digital waveforms in one dimension or images in two dimensions has a long and storied history. The basic idea in digital image filtering is to post-process an image using standard techniques culled from signal processing theory. An analogy may be drawn to the type of filters one might employ in traditional photography. An optical filter placed on a camera's lens is used to accentuate or attenuate certain global characteristics of the image seen on film. For example, photographers may use a red filter to separate plants from a background of mist or haze, and most professional photographers use a polarizing filter for glare removal1. Optical filters work their magic in the analog domain (and hence can be thought of as analog filters), whereas in this chapter we implement digital filters to process digital images.
A common means of filtering images is through the use of "sliding neighborhood processing", where a "mask" is slid across the input image and at each point, an output pixel is computed using some formula that combines the pixels within the current neighborhood. This type of processing is shown diagrammatically in Figure 4-1, which is a high-level...
