To process images on computers, the images must be sampled to create digital images. This represents a transformation from the analog domain to the discrete domain. This chapter will concentrate on the very basic step of sampling an image in preparation for processing. It will be shown that this step is crucial, in that sampling imposes strict limits on the processing that can be done and the fidelity of any reconstructions.

Images for most consumer and commercial uses are the color images that we see every day. These images are transformations of continuously varying spectral, temporal and spatial distributions. In this chapter, we will address the problems of spatial sampling. Thus, it is sufficient to use monochrome images to demonstrate the principles. In Chapter 9, we will discuss sampling in the spectral dimension. The principles for color spectral sampling are an extension of those that we will cover in this chapter.

All images exist in time and change with time. We are all familiar with the stroboscopic effects that we see in the movies and television that make car wheels and airplane propellers appear to move backwards. The same sampling principles can be used to explain these phenomena as will be used to explain the spatial sampling that is presented here. The description of object motion in time and its effect on images is another rich topic that will be left for other texts, e.g., [32, 83, 262, 302].