外文文献及翻译--一种有效地自动图像增强方法

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1、附录 A：外文文献 An Effective Automatic Image Enhancement Method ABSTRACT 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 differences among

2、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 automatic parameter s

3、election 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 between object and b

4、ackground. 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 visual inspection

5、 1 Introduction Automated 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 classification. Imag

6、e 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 background. Gonzale

7、z 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 quality. Cente

8、no 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 function. In additio

9、n 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 on Haar wavele

10、t 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 of the system

11、. 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 (PCNN). Salzenste

12、in 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 12 demonstrat

13、es 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 optimal parameter f

14、or 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.2 presents h

15、ow 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. 2 Image Enhancement 2.1 Analysis of Prior Knowledge The preprocessing quality influences the latter work directly, i

16、n 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, appears withi

17、n 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) is a sta

18、ndard 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 Formulation This section discusses the principal content in the paper. Traditional spatial filter uses a 33 mask, the elements of which sum to unity, to convolve with t

19、he input image. This method can deal with some cases shown in equation (1): ( , ) ( , ) ( , )G x y I x y N x y （ 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 w

20、hite noise, but any other background uninteresting. Thus equation (1) is improved by equation (2): ( , ) ( , ) ( , )G x y I x y N x y （ 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 illustra

21、te that tuning Para properly is to facilitate object segmentation. The smoothing function used is shown in equation (3): 1111( , ) ( , ) ( , )fmnI x y G x y F x m y n （ 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 d

22、efine Mg as the maximum gray level of an image. Then the following equations are set to distinguish the object of interest and the non-object : ,f f gf g f gI i f I MIM i f I M （ 4） In essence, convolution operator is a low-pass filtering process, which blurs an image by sliding a mask through the i

23、mage and leaves the filtering response at the position corresponding to central location of the mask. One question occurs that, why not enhance value 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 rel

24、ationship of adjacent pixels. When individual noise point occur, enhancing its gray value directly will preserve the noise point. Experiments illustrate 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 s

25、o 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 is denoted by ni and the total number of pixels by N. The probability of each level is denoted by Pi as follow 9: 1/ , 0 , 1li i i iiP n N P P （ 5） Supp

26、ose 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 levels k+1, , L. Then the probabilities of class occurrence w0,w1 and the class mean levels u0,u1 respectively,are given by 0 1=nii Pk （ 6）