图像处理外文翻译

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1、附录 A 3 Image Enhancement in the Spatial Domain The principal objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application. The word specific is important, because it establishes at the outset than the techniques discussed in t

2、his chapter are very much problem oriented. Thus, for example, a method that is quite useful for enhancing X-ray images may not necessarily be the best approach for enhancing pictures of Mars transmitted by a space probe. Regardless of the method used .However, image enhancement is one of the most i

3、nteresting and visually appealing areas of image processing. Image enhancement approaches fall into two broad categories: spatial domain methods and frequency domain methods. The term spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of

4、 pixels in an image. Fourier transform of an image. Spatial methods are covered in this chapter, and frequency domain enhancement is discussed in Chapter 4.Enhancement techniques based on various combinations of methods from these two categories are not unusual. We note also that many of the fundame

5、ntal techniques introduced in this chapter in the context of enhancement are used in subsequent chapters for a variety of other image processing applications. There is no general theory of image enhancement. When an image is processed for visual interpretation, the viewer is the ultimate judge of ho

6、w well a particular method works. Visual evaluation of image quality is a highly is highly subjective process, thus making the definition of a “good image” an elusive standard by which to compare algorithm performance. When the problem is one of processing images for machine perception, the evaluati

7、on task is somewhat easier. For example, in dealing with a character recognition application, and leaving aside other issues such as computational requirements, the best image processing method would be the one yielding the best machine recognition results. However, even in situations when a clear-c

8、ut criterion of performance can be imposed on the problem, a certain amount of trial and error usually is required before a particular image enhancement approach is selected. 3.1 Background As indicated previously, the term spatial domain refers to the aggregate of pixels composing an image. Spatial

9、 domain methods are procedures that operate directly on these pixels. Spatial domain processes will be denotes by the expression , ( , )g x y T f x y (3.1-1) where f(x, y) is the input image, g(x, y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). In addi

10、tion, T can operate on a set of input images, such as performing the pixel-by-pixel sum of K images for noise reduction, as discussed in Section 3.4.2. The principal approach in defining a neighborhood about a point (x, y) is to use a square or rectangular subimage area centered at (x, y).The center

11、 of the subimage is moved from pixel to starting, say, at the top left corner. The operator T is applied at each location (x, y) to yield the output, g, at that location. The process utilizes only the pixels in the area of the image spanned by the neighborhood. Although other neighborhood shapes, su

12、ch as approximations to a circle, sometimes are used, square and rectangular arrays are by far the most predominant because of their ease of implementation. The simplest from of T is when the neighborhood is of size 11 (that is, a single pixel). In this case, g depends only on the value of f at (x,

13、y), and T becomes a gray-level (also called an intensity or mapping) transformation function of the form ()s T r (3.1-2) where, for simplicity in notation, r and s are variables denoting, respectively, the grey level of f(x, y) and g(x, y)at any point (x, y).Some fairly simple, yet powerful, process

14、ing approaches can be formulates with gray-level transformations. Because enhancement at any point in an image depends only on the grey level at that point, techniques in this category often are referred to as point processing. Larger neighborhoods allow considerably more flexibility. The general ap

15、proach is to use a function of the values of f in a predefined neighborhood of (x, y) to determine the value of g at (x, y). One of the principal approaches in this formulation is based on the use of so-called masks (also referred to as filters, kernels, templates, or windows). Basically, a mask is

16、a small (say, 33) 2-Darray, in which the values of the mask coefficients determine the nature of the type of approach often are referred to as mask processing or filtering. These concepts are discussed in Section 3.5. 3.2 Some Basic Gray Level Transformations We begin the study of image enhancement

17、techniques by discussing gray-level transformation functions. These are among the simplest of all image enhancement techniques. The values of pixels, before and after processing, will be denoted by r and s, respectively. As indicated in the previous section, these values are related by an expression

18、 of the from s = T(r), where T is a transformation that maps a pixel value r into a pixel value s. Since we are dealing with digital quantities, values of the transformation function typically are stored in a one-dimensional array and the mappings from r to s are implemented via table lookups. For a

19、n 8-bit environment, a lookup table containing the values of T will have 256 entries. As an introduction to gray-level transformations, which shows three basic types of functions used frequently for image enhancement: linear (negative and identity transformations), logarithmic (log and inverse-log t

20、ransformations), and power-law (nth power and nth root transformations). The identity function is the trivial case in which out put intensities are identical to input intensities. It is included in the graph only for completeness. 3.2.1 Image Negatives The negative of an image with gray levels in th

21、e range 0, L-1is obtained by using the negative transformation show shown, which is given by the expression 1s L r (3.2-1) Reversing the intensity levels of an image in this manner produces the equivalent of a photographic negative. This type of processing is particularly suited for enhancing white or grey detail embedded in dark regions of an image, especially