外文文献及翻译一种有效地自动图像增强方法.docx

1、外文文献及翻译一种有效地自动图像增强方法附录A：外文文献An Effective Automatic Image Enhancement MethodABSTRACT Otsu method is proper to deal with two conditions: (1) two or more classes with distintive gray-values respectively; (2) classes without distinctive gray-values, but with similar areas. However, when the gray-value d

2、ifferences among classes are not so distinct, and the object is small relative to backgroud, the separabilities among classes are insufficient. In order to overcome the above problem, this paper presents an improved spatial low-pass filter with a parameter and presents an unsupervised method of auto

3、matic parameter selection for image enhancement based on Otsu method. This method combines image enhancement with image segmentation as one procedure through a discriminant criterion. The optimal parameter of the filter is selected by the discriminant criterion given to maximize the separability bet

4、ween object and background. The optimal threshold for image segmentation is computed simultaneously. The method is used to detect the surface defect of container. Experiments illustrate the validity of the method.KEYWORDS image processing; automated image enhancement; image segmentation; automated v

5、isual inspection1 IntroductionAutomated visual inspection of cracked container (AVICC) is a practical application of machine vision technology. To realize our goal, four essential operations must be dealt with image preprocessing, object detection, feature description and final cracked object classi

6、fication. Image enhancement is to provide a result more suitable than original image for specific applications. In this paper the objective of enhancement, followed by image segmentation, is to obtain an image with a higher content about the object interesting with less content about noise and backg

7、round. Gonzalez 1 discusses that image enhancement approaches fall into two main categories, in that spatial domain and frequency domain methods. Burton 2 applies image averaging technique to face recognition system, making it able to recognise familiar faces easily across large variations in image

8、quality. Centeno 3 proposes an adaptive image enhancement algorithm, which reverse the processing order of image enhancement and segmentation in order to avoid sharpening noise and blurring borders. Munteanu 4 applies artificial intelligence technology to image enhancement providing denoising functi

9、on. In addition to spatial domain methods, frequency domain processing techniques are based on modifying the Fourier transform of an image. Bakir 5 discusses image enhancement used for medical image processing in frequency space. Besides, Wang 6 presents a global multiscale analysis of images based

10、on Haar wavelet technique for image denoising. Recently, Agaian 7 proposes image enhancement methods based on the properties of the logarithmic transform domain histogram and histogram equalization. We apply spatial processing here in order to guarantee the real-time and sufficient accuracy property

11、 of the system.Segmentation is discussed in 8. The most simplest, represented by Otsu 9, is method using only the gray level histogram analysis to maximize the separability of the resultant classes. Kuntimad 10 describes a method for segmenting digital images using pulse coupled neural networks (PCN

12、N). Salzenstein 11 deals with a comparison of recent statistical models on fuzzy Markov random fields and chains for multispectral image segmentation. Due to ill-defined, there is no unique segmentation of an image. Evaluation of segmentation algorithms thus far has been largely subjective. Ranjith

13、12 demonstrates how a recently proposed measureof similarity can be used to perform a quantitative comparison among image segmentation algorithms. In this paper, we present an improved spatial low-pass filter with a tunable parameter in the mask making all elements no longer sum to unity. The optima

14、l parameter for the filter can be determined by the improved discriminant criterion based on the one mentioned in 9. Convolving images with this mask, the background uninteresting can be removed easily leaving the object intact to some extent. The remainder of the paper is organized as follows: Sect

15、.2 presents how to enhance an input image in theory and presents the algorithm. Sect.3 illustrates the validity of the method in Sect.2. Finally, conclusion and discussion are presented in Sect.4.2Image Enhancement2.1 Analysis of Prior KnowledgeThe preprocessing quality influences the latter work di

16、rectly, in that, feature description. Therefore, analysis for the characteristics related to input images should be presented. A standard image of cracked container is shown as Fig.1 (a). From the image, we see the cracked part occupies small region. Much noise, such as rust, shadow, smear etc, appe

17、ars within the background. At a coarse glance, however, we find gray level of the hole is less than the other parts distinctly. Further study shows gray level of pixels, around the edge of the hole, is the minimal. Fig.1(b) displays the histogram of Fig.1(a) and edge of the hole is marked.Fig.1 (a)

18、is a standard gray level image of a cracked container(b) is the histogram of Fig.1 (a), indicating gray level region of the holes edge. 2.2 FormulationThis section discusses the principal content in the paper. Traditional spatial filter uses a 33 mask, the elements of which sum to unity, to convolve

19、 with the input image. This method can deal with some cases shown in equation (1): （1） where, I is image interested, N is Gaussian white noise, (x,y) denotes each pair of coordinates. N can be deliminated by blurring G. Our objective, however, is to deliminate not only white noise, but any other bac

20、kground uninteresting. Thus equation (1) is improved by equation (2): （2）where, I is the object, N consists of white noise and the other parts except I. Fig.2 (c) displays an improved mask with a parameter Para. We will later illustrate that tuning Para properly is to facilitate object segmentation.

21、 The smoothing function used is shown in equation (3): （3）where, F(x,y) denotes the smoothing filter, in that, the mask shown as Fig.2 (c). Now, we only consider gray-level images, and define Mg as the maximum gray level of an image. Then the following equations are set to distinguish the object of

22、interest and the non-object : （4） In essence, convolution operator is a low-pass filtering process, which blurs an image by sliding a mask through the image and leaves the filtering response at the position corresponding to central location of the mask. One question occurs that, why not enhance valu

23、e of each pixel by the same scale directly for the distinct gray levels between the object and background. The reason is that it doesnt consider the relationship of adjacent pixels. When individual noise point occur, enhancing its gray value directly will preserve the noise point. Experiments illust

24、rate the latter method will leave lots of noise points cant be removed, but the former method will not. Now, we will search the optimal parameter Para so as to maximize the separability between object and background. Let a given image be represented in L gray levels. The number of pixels at level i

25、is denoted by ni and the total number of pixels by N. The probability of each level is denoted by Pi as follow 9: （5） Suppose that we partition the pixels into two classes C0 and C1 (object and background) by a threshold at level k; C0 denotes pixels with levels 1, , k, and C1 denotes pixels with le

26、vels k+1, , L. Then the probabilities of class occurrence w0,w1 and the class mean levels u0,u1 respectively,are given by （6） （7） （8） （9） （10） （11） （12）The procedure of obtaining optimal para is based on obtaining optimal threshold for every filtered image. The optimal threshold is determined by max

27、imizing the separability between object and background using the following discriminant criterion measure as mentioned in 9 : （13）where （14）andare the between class variance and the total variance of levels,respectively. （15）The optimal threshold k* that maximizes n is selected in the following sequ

28、ential search by using equation (5)-(14): （16）Equation (16) is a discriminant criterion to select the gray level to maximize the separability between object and background for a given picture. In this paper, a parameter Para is introduced, so the equations (6)(9), (11)(14), (16) is parameterized by

29、Para and k and equations (10), (15) is parameterized by Para. Equation (13) can be rewritten as: （17）Where is not a constant any more and is not negligible, but some computation reduction can be operated onandHere, what we want to acquire is the proper filtered picture including vivid object by sear

30、ching parameter Para, the discriminant criterion used is improved as follow : （18）In the above representation, parameter Para plays an important role, because optimal Para makes the separability between object and background maximal, and make Otsu segmentation method effective to segment small objec

31、t from large background without distinctive gray-value between them, which can be observed later from image histogram after image enhancement2.3 Existence Discussion of Para and k*The problem above is reduced to search for a threshold k* under the condition of Para which maximizes the discriminant c

32、riterion in equation (18). The condition discussed is the image with two class at least. Subsequently, the following two cases dont occur, in that,(1) w0 or w1 is zero originally without setting Para,in which there is only one class;(2) w0 or w1 is zero with certain increasing Para,in which there is also one class finally;The above two cases are decribed as:The case concerned is A,Thus,there is certain Para with proper k to make discriminant criterion maximal.3 ExperimentsThis paper