【正文】 which they are applied. Images based on radiation from the EM spectrum are the most familiar, es pecially images in the Xray and visual bands of the spectrum. Electromag ic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other. Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling. Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” enhancement result. Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Inter. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image. Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data pression and for pyramidal representation, in which images are subdivided successively into smaller regions. Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmi storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Inter, which are characterized by significant pictorial content. Image pression is familiar (perhaps inadvertently) to most users of puters in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image pression standard. Morphological processing deals with tools for extracting image ponents that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes. Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the bound ary of a region (., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for puter processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a plete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations plement each other. Choosing a representation is only part of the solution for trans forming raw data into a form suitable for subsequent puter processing. A meth