TABLE OF CONTENTS Broadly speaking, image enhancement algorithms fall into two categories: those that operate in the "spatial domain", and those that operate in the "frequency domain". Spatial processing of images works by operating directly on an image's pixel values. In contrast, frequency domain methods use mathematical tools such as the Discrete Fourier Transform to convert the 2D function an image represents into an alternate formulation consisting of coefficients correlating to spatial frequencies. These frequency coefficients are subsequently manipulated, and then the inverse transform is applied to map the frequency coefficients back to gray-level pixel intensities.
In this chapter, we discuss a variety of common spatial image processing algorithms and then implement these algorithms on the DSP. Image enhancement per se sometimes means more than enhancing the subjective quality of a digital image, as viewed from the vantage point of a human observer. The term may also refer to the process of accentuating certain characteristics of an image at the expense of others, for the purpose of further analysis (by either a computer or human being).
Spatial processing methods are one of the most common forms of image processing, as they are relatively simple but quite effective. The general idea is to enhance the appearance of an image f by applying a gray-level transformation function T that maps the input pixels of f to output pixels in an image g, as shown in Figure 3-1.
