图像处理外文翻译Word文档格式.docx

1、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 this chapter are very much problem oriented. Thu

2、s, 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 interesting and visually appealing areas of imag

3、e 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 pixels in an image. Fourier transform of an ima

4、ge. 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 fundamental techniques introduced in this chapter in th

5、e 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 how well a particular method works. Visual evaluati

6、on 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 evaluation task is somewhat easier. For example, in deali

7、ng 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-cut criterion of performance can be imposed on the

8、 problem, a certain amount of trial and error usually is required before a particular image enhancement approach is selected.3.1 BackgroundAs indicated previously, the term spatial domain refers to the aggregate of pixels composing an image. Spatial domain methods are procedures that operate directl

9、y on these pixels. Spatial domain processes will be denotes by the expression （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 addition, T can operate on a set of input images, such as performing the pixe

10、l-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 of the subimage is moved from pixel to starting, say, at the top left cor

11、ner. 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, such as approximations to a circle, sometimes are used, square and rectangul

12、ar 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, y）, and T becomes a gray-level （also called an intensity or mapping） transf

13、ormation function of the form （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, processing approaches can be formulates with gray-level transformations. Because enhancemen

14、t 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 approach is to use a function of the values of f in a predefined neighborhood of （x, y）

15、 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 a small （say, 33） 2-Darray, in which the values of the mask coefficients determine th

16、e 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 TransformationsWe begin the study of image enhancement techniques by discussing gray-level transformation functions. These are among the simpl

17、est 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 of the from s = T（r）, where T is a transformation that maps a pixel value r into a pix

18、el 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 an 8-bit environment, a lookup table containing the values of T will have 256 entries.As

19、 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 transformations）, and power-law （nth power and nth root transformations）. The identity fu

20、nction 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 NegativesThe negative of an image with gray levels in the range 0, L-1is obtained by using the negative transformation show shown, which is given

21、by the expression （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 when the black areas are dominant

22、 in size. 3.2.2 Log TransformationsThe general from of the log transformation is （3.2-2） Where c is a constant, and it is assumed that r 0 .The shape of the log curve transformation maps a narrow range of low gray-level values in the input image into a wider range of output levels. The opposite is t

23、rue of higher values of input levels. We would use a transformation of this type to expand the values of dark pixels in an image while compressing the higher-level values. The opposite is true of the inverse log transformation.Any curve having the general shape of the log functions would accomplish

24、this spreading/compressing of gray levels in an image. In fact, the power-law transformations discussed in the next section are much more versatile for this purpose than the log transformation. However, the log function has the important characteristic that it compresses the dynamic range of image c

25、haracteristics of spectra. It is not unusual to encounter spectrum values that range from 0 to 106 or higher. While processing numbers such as these presents no problems for a computer, image display systems generally will not be able to reproduce faithfully such a wide range of intensity values .Th

26、e net effect is that a significant degree of detail will be lost in the display of a typical Fourier spectrum.3.2.3 Power-Law TransformationsPower-Law transformations have the basic from （3.2-3） Where c and y are positive constants .Sometimes Eq. （3.2-3） is written as to account for an offset （that

27、is, a measurable output when the input is zero）. However, offsets typically are an issue of display calibration and as a result they are normally ignored in Eq. （3.2-3）. Plots of s versus r for various values of y are shown in Fig.3.6. As in the case of the log transformation, power-law curves with

28、fractional values of y map a narrow range of dark input values into a wider range of output values, with the opposite being true for higher values of input levels. Unlike the log function, however, we notice here a family of possible transformation curves obtained simply by varying y. As expected, w

29、e see in Fig.3.6 that curves generated with values of y1 have exactly the opposite effect as those generated with values of y1. Finally, we note that Eq.（3.2-3） reduces to the identity transformation when c = y = 1.A variety of devices used for image capture, printing, and display respond according

30、to as gammahence our use of this symbol in Eq.（3.2-3）.The process used to correct this power-law response phenomena is called gamma correction.Gamma correction is important if displaying an image accurately on a computer screen is of concern. Images that are not corrected properly can look either bl

31、eached out, or, what is more likely, too dark. Trying to reproduce colors accurately also requires some knowledge of gamma correction because varying the value of gamma correcting changes not only the brightness, but also the ratios of red to green to blue. Gamma correction has become increasingly i

32、mportant in the past few years, as use of digital images for commercial purposes over the Internet has increased. It is not Internet has increased. It is not unusual that images created for a popular Web site will be viewed by millions of people, the majority of whom will have different monitors and/or monitor settings. Some computer systems even have partial gamma correction built in. Also, current image standards do not contain the value of gamma with which an image was created, thus complicating the issue further. Given these constraints, a reasonable appr