数字图像处理英文文献翻译参考0000

Hybrid Genetic Algorithm Based Image Enhancement

Technology

Abstract:In image enhancement, Tubbs proposed a normalized incomplete Beta

function to represent several kinds of commonly used non-linear transform functions to do the research on image enhancement. But how to define the coefficients of the Beta function is still a problem. We proposed a Hybrid Genetic Algorithm which combines the Differential Evolution to the Genetic Algorithm in the image enhancement process and utilize the quickly searching ability of the algorithm to carry out the adaptive mutation and searches. Finally we use the Simulation experiment to prove the effectiveness of the method.

Keywords:Image enhancement; Hybrid Genetic Algorithm; adaptive enhancement

I. INTRODUCTION

In the image formation, transfer or conversion process, due to other objective factors such as system noise, inadequate or excessive exposure, relative motion and so the impact will get the image often a difference between the original image (referred to as degraded or degraded) Degraded image is usually blurred or after the extraction of information through the machine to reduce or even wrong, it must take some measures for its improvement.

Image enhancement technology is proposed in this sense, and the purpose is to improve the image quality. Fuzzy Image Enhancement situation according to the image using a variety of special technical highlights some of the information in the image, reduce or eliminate the irrelevant information, to emphasize the image of the whole or the purpose of local features. Image enhancement method is still no unified theory, image enhancement techniques can be divided into three categories: point operations, and spatial frequency enhancement methods Enhancement Act. This paper presents an automatic adjustment according to the image characteristics of adaptive image enhancement method that called hybrid genetic algorithm. It combines the differential evolution algorithm of adaptive search capabilities, automatically determines the transformation function of the parameter values in order to achieve adaptive image enhancement.

II. IMAGE ENHANCEMENT TECHNOLOGY

Image enhancement refers to some features of the image, such as contour, contrast, emphasis or highlight edges, etc., in order to facilitate detection or further analysis and processing. Enhancements will not increase the information in the image data, but will choose the appropriate features of the expansion of dynamic range, making these features

more easily detected or identified, for the detection and treatment follow-up analysis and lay a good foundation.

Image enhancement method consists of point operations, spatial filtering, and frequency domain filtering categories. Point operations, including contrast stretching, histogram modeling, and limiting noise and image subtraction techniques. Spatial filter including low-pass filtering, median filtering, high pass filter (image sharpening). Frequency filter including homomorphism filtering, multi-scale multi-resolution image enhancement applied [1].

III. DIFFERENTIAL EVOLUTION ALGORITHM

Differential Evolution (DE) was first proposed by Price and Storn, and with other evolutionary algorithms are compared, DE algorithm has a strong spatial search capability, and easy to implement, easy to understand. DE algorithm is a novel search algorithm, it is first in the search space randomly generates the initial population and then calculate the difference between any two members of the vector, and the difference is added to the third member of the vector, by which Method to form a new individual. If you find that the fitness of new individual members better than the original, then replace the original with the formation of individual self.

The operation of DE is the same as genetic algorithm, and it conclude mutation, crossover and selection, but the methods are different. We suppose that the group size is P, the vector dimension is D, and we can express the object vector as (1):

xi=[xi1，xi2，…，xiD] (i =1,…,P) (1) And the mutation vector can be expressed as (2):

Vi?Xr1?F??Xr2?Xr3? i=1,...,P (2)

Xr1,Xr2,Xr3are three randomly selected individuals from group, and r1?r2?r3?i.F

is a range of [0, 2] between the actual type constant factor difference vector is used to control the influence, commonly referred to as scaling factor. Clearly the difference between the vector and the smaller the disturbance also smaller, which means that if groups close to the optimum value, the disturbance will be automatically reduced.

DE algorithm selection operation is a \vector ui the fitness of the individual than the target vector is better when the individual xi, ui will be retained to the next group. Otherwise, the target vector xi individuals remain in the original group, once again as the next generation of the parent vector.

IV. HYBRID GA FOR IMAGE ENHANCEMENT IMAGE

enhancement is the foundation to get the fast object detection, so it is necessary to find real-time and good performance algorithm. For the practical requirements of different systems, many algorithms need to determine the parameters and artificial thresholds. Can use a non-complete Beta function, it can completely cover the typical image enhancement

transform type, but to determine the Beta function parameters are still many problems to be solved. This section presents a Beta function, since according to the applicable method for image enhancement, adaptive Hybrid genetic algorithm search capabilities, automatically determines the transformation function of the parameter values in order to achieve adaptive image enhancement.

The purpose of image enhancement is to improve image quality, which are more prominent features of the specified restore the degraded image details and so on. In the degraded image in a common feature is the contrast lower side usually presents bright, dim or gray concentrated. Low-contrast degraded image can be stretched to achieve a dynamic histogram enhancement, such as gray level change. We use Ixy to illustrate the gray level of point (x, y) which can be expressed by (3).

Ixy=f(x, y) (3) where: “f” is a linear or nonlinear function. In general, gray image have four nonlinear translations [6] [7] that can be shown as Figure 1. We use a normalized incomplete Beta function to automatically fit the 4 categories of image enhancement transformation curve. It defines in (4):

f?u??B?1??,???t??1?1?t?dt???0,??10? (4)

0u??1where:

B??,????t??1?1?t?01??1dt (5)

For different value of α and β, we can get response curve from (4) and (5).

The hybrid GA can make use of the previous section adaptive differential evolution algorithm to search for the best function to determine a value of Beta, and then each pixel grayscale values into the Beta function, the corresponding transformation of Figure 1, resulting in ideal image enhancement. The detail description is follows:

Assuming the original image pixel (x, y) of the pixel gray level by the formula (4), denoted byixy,?x,y???, here Ω is the image domain. Enhanced image is denoted by Ixy. Firstly, the image gray value normalized into [0, 1] by (6).

ixy?imingxy?imax?imin(6)

where: imaxand iminexpress the maximum and minimum of image gray relatively.

Define the nonlinear transformation function f(u) (0≤u≤1) to transform source image to Gxy=f(gxy), where the 0≤ Gxy ≤ 1.

Finally, we use the hybrid genetic algorithm to determine the appropriate Beta function f (u) the optimal parameters α and β. Will enhance the image Gxy transformed antinormalized.

V. EXPERIMENT AND ANALYSIS

In the simulation, we used two different types of gray-scale images degraded; the program performed 50 times, population sizes of 30, evolved 600 times. The results show that the proposed method can very effectively enhance the different types of degraded image.

Figure 2, the size of the original image a 320 × 320, it's the contrast to low, and some details of the more obscure, in particular, scarves and other details of the texture is not obvious, visual effects, poor, using the method proposed in this section, to overcome the above some of the issues and get satisfactory image results, as shown in Figure 5 (b) shows, the visual effects have been well improved. From the histogram view, the scope of the distribution of image intensity is more uniform, and the distribution of light and dark gray area is more reasonable. Hybrid genetic algorithm to automatically identify the nonlinear transformation of the function curve, and the values obtained before 9.837,5.7912, from the curve can be drawn, it is consistent with Figure 3, c-class, that stretch across the middle region compression transform the region, which were consistent with the histogram, the overall original image low contrast, compression at both ends of the middle region

stretching region is consistent with human visual sense, enhanced the effect of significantly improved.

Figure 3, the size of the original image a 320 × 256, the overall intensity is low, the use of the method proposed in this section are the images b, we can see the ground, chairs and clothes and other details of the resolution and contrast than the original image has Improved significantly, the original image gray distribution concentrated in the lower region, and the enhanced image of the gray uniform, gray before and after transformation and nonlinear transformation of basic graph 3 (a) the same class, namely, the image Dim region stretching, and the values were 5.9409,9.5704, nonlinear transformation of images degraded type inference is correct, the enhanced visual effect and good robustness enhancement.

Difficult to assess the quality of image enhancement, image is still no common evaluation criteria, common peak signal to noise ratio (PSNR) evaluation in terms of line, but the peak signal to noise ratio does not reflect the human visual system error. Therefore, we use marginal protection index and contrast increase index to evaluate the experimental results.

Edgel Protection Index (EPI) is defined as follows:

Contrast Increase Index (CII) is defined as follows:

(7)

E?G?GminCD,C?maxCOGmax?Gmin (8)

In figure 4, we compared with the Wavelet Transform based algorithm and get the

evaluate number in TABLE I.

Figure 4 (a, c) show the original image and the differential evolution algorithm for enhanced results can be seen from the enhanced contrast markedly improved, clearer image details, edge feature more prominent. b, c shows the wavelet-based hybrid genetic algorithm-based Comparison of Image Enhancement: wavelet-based enhancement method to enhance image detail out some of the image visual effect is an improvement over the original image, but the enhancement is not obvious; and Hybrid genetic algorithm based on adaptive transform image enhancement effect is very good, image details, texture, clarity is enhanced compared with the results based on wavelet transform has greatly improved the image of the post-analytical processing helpful. Experimental enhancement experiment using wavelet transform \wavelet, enhanced differential evolution algorithm experiment, the parameters and the values were 5.9409,9.5704. For a 256 × 256 size image transform based on adaptive hybrid genetic algorithm in Matlab 7.0 image enhancement software, the computing time is about 2 seconds, operation is very fast. From TABLE I, objective evaluation criteria can be seen, both the edge of the protection index, or to enhance the contrast index, based on adaptive hybrid genetic algorithm compared to traditional methods based on wavelet transform has a larger increase, which is from This section describes the objective advantages of the method. From above analysis, we can see that this method.

From above analysis, we can see that this method can be useful and effective.

VI. CONCLUSION

In this paper, to maintain the integrity of the perspective image information, the use of Hybrid genetic algorithm for image enhancement, can be seen from the experimental results, based on the Hybrid genetic algorithm for image enhancement method has obvious effect. Compared with other evolutionary algorithms, hybrid genetic algorithm outstanding performance of the algorithm, it is simple, robust and rapid convergence is almost optimal solution can be found in each run, while the hybrid genetic algorithm is only a few parameters need to be set and the same set of parameters can be used in many different problems. Using the Hybrid genetic algorithm quick search capability for a given test image adaptive mutation, search, to finalize the transformation function from the best parameter values. And the exhaustive method compared to a significant reduction in the time to ask and solve the computing complexity. Therefore, the proposed image enhancement method has some practical value.

REFERENCES

[1] HE Bin et al., Visual C++ Digital Image Processing [M], Posts & Telecom Press, 2001,4:473～477

[2] Storn R, Price K. Differential Evolution—a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Space[R]. International Computer Science Institute, Berlaey, 1995. [3] Tubbs J D. A note on parametric image enhancement [J].Pattern Recognition.1997, 30(6):617-621.

[4] TANG Ming, MA Song De, XIAO Jing. Enhancing Far Infrared Image Sequences with Model Based Adaptive Filtering [J] . CHINESE JOURNAL OF COMPUTERS, 2000, 23(8):893-896.

[5] ZHOU Ji Liu, LV Hang, Image Enhancement Based on A New Genetic Algorithm [J]. Chinese Journal of Computers, 2001, 24(9):959-964.

[6] LI Yun, LIU Xuecheng. On Algorithm of Image Constract Enhancement Based on Wavelet Transformation [J]. Computer Applications and Software, 2008,8. [7] XIE Mei-hua, WANG Zheng-ming, The Partial Differential Equation Method for Image Resolution Enhancement [J]. Journal of Remote Sensing, 2005，9(6):673-679.

基于混合遗传算法的图像增强技术

摘要:在图像增强之中，塔布斯提出了归一化不完全β函数表示常用的几种使用的

非线性变换函数对图像进行研究增强。但如何确定Beta系数功能仍然是一个问题。在图像增强处理和利用遗传算法快速算法的搜索能力进行自适应变异和搜索我们提出了一种混合遗传将微分进化算法。最后利用仿真实验证明了该方法的有效性。

关键词:图像增强；混合遗传算法；自适应增强

Ⅰ.介绍

在图像形成，传递或转换过程，由于其他客观因素，如系统噪声，不足或过度

曝光，相对运动等的影响会使图像通常与原始图像之间有差别（简称退化或退化）。退化图像通常模糊或信息的提取通过机器后减少甚至是错误的，它必须采取一些改进措施。

图像增强技术是在其目的是为了提高图像的质量这个意义上提出的。模糊图像增强情况是根据图像使用各种特殊技术集锦的一些信息图像，减少或消除不相关的信息，来强调整体或局部特征的目标图像。图像增强方法仍没有统一的理论，图像增强技术可分为三类别：点运算，与空间频率增强方法增强法。本文介绍了根据图像特征自动调整自适应图像增强方法，称为混合遗传算法。为了实现图像的自适应增强它结合了差分进化自适应搜索算法，自动确定的参数值的变换函数。

Ⅱ.图像增强技术

图像增强是图像的某些特征，如轮廓，对比，强调或突出的边缘等为了便于检

测和进一步的分析和处理. 增强将不会增加图像中的信息数据，但会选择适当的动态范围的功能的扩展，使得这些特点更容易检测或确定，为后续的分析和处理的检测打下良好的基础。

图像增强方法包括点运算，空间滤波，频域滤波类别。点运算包括对比度拉伸，直方图建模，并限制噪声和图像减影技术。空间滤波器包括低通滤波，中值滤波，高通滤波器（锐化）。频率滤波器包括同态滤波，多尺度多分辨率图像增强中的应用[1]。

Ⅲ.差分进化算法

差分进化（DE）首次提出了强硬的价值，并与其他进化算法进行比较，DE算法具有强大的空间搜索能力，易实现，容易理解。DE算法是一种新型的搜索算法，它首先是在搜索空间中随机产生初始种群，然后计算之间的任何差异向量的两个成员，所不同的添加到向量的第三个成员，通过该方法，形成一个新的个人。如果你发现新的个体成员比原来的好，然后替换原来的个体，自我的形成。

DE操作作为遗传算法一样，它结论突变，交叉和选择，但方法是不同的。我们假设组的大小是P，矢量维D，我们可以表达的目标向量为（1）：

xi=[xi1，xi2，?，xiD] (i =1,?,P) (1) 变异向量可以表示为（2）：

Vi?Xr1?F??Xr2?Xr3? i=1,...,P (2)

Xr1,Xr2,Xr3是三个从群中随机选择的个人 ，其中，r1?r2?r3?i。F是一系列

的[ 0，2 ] 之间的实际类型的用于控制影响的常数因子差异向量，通常被称

为比例因子。 显然，矢量之间的区别越小则干扰也越小，这意味着如果组接近最佳值，扰动会自动降低。

DE算法的选择操作是一个“贪婪”的选择模式，当且仅当新的矢量Ui比目标向量Xi更好更健全，Ui将被保留到下一组。否则，目标向量Xi留在原来的组，再次作为下一代的父矢量。

Ⅳ.图像增强图像的混合遗传算法

增强是获得快速对象检测的基础，因此有必要寻找实时性能好的算法。对不同

系统的实际要求，许多算法需要确定的参数和人工阈值。它可以使用一个非完全Beta函数来完全覆盖典型变换式的图像增强，但确定Beta函数参数仍有许多亟待解决的问题。本节介绍了一种Beta功能，因为根据适用的图像增强的方法，自适应混合遗传算法的搜索的能力，自动确定变换命令的参数值来实现图像增强的自适应功能。

图像增强的目的是提高图像质量，是在指定的比较突出的特点恢复退化图像细节等。一个共同的特征的退化图像通常是对比的下侧呈明亮的，暗淡或灰色浓。低对比度退化图像可拉伸达到一种动态的直方图增强，如灰度变化。我们用 Ixy来说明点（x，y）的灰度级它可以是由（3）表示。

Ixy=f(x,y) (3) 其中：“f”为一个线性或非线性函数。在一般情况下，灰图像有四个非线性的翻译[6] [7]，可以是如图1所示。我们采用归一化的 Beta函数自动适应4类图像增强转变曲线。（4）中定义：

f?u??B?1??,???t??1?1?t?dt???0,??10? (4)

0u??1其中：

B??,????t??1?1?t?01??1dt (5)

对于不同的α，β值，我们可以从（4）及（5）中得到响应曲线。

图1 四种传统的翻译

该混合算法可以利用前面的部分自适应差分进化算法搜索最佳函数来确定的β值，然后每个像素灰度值为β函数，相应的图1转化，产生理想的图像增强。详细描述如下：

假设原始图像的像素（x，y）的像素的灰度水平，表示为式（4），记为ixy，?x,y???，这里?是图像域。增强的图像由Ixy表示。首先，图像的灰度值在（6）中归到[0，1]。

ixy?imingxy?imax?imin(6)

其中：imax和imin表示图像灰度的最大值和最小值。

定义非线性变换函数f（U）（0≤U≤1）变换成源图像GXY=f（GXY），其中，0

≤GXY≤1。最后，我们使用了混合遗传算法来确定适当的Beta函数f（U）的最佳参数α和β。

V.实验和分析

在模拟中，我们使用两种不同类型的灰度图像退化；程序执行了50次，人口大小为30，进化600次。结果表明，提出的方法可以非常有效地提高不同退化图像类型。

a) 原始图像 b) 增强图像

图2 单个图像增强过程

a) 原始图像 b) 增强图像

图3 移动对象增强过程

图2，原始图像为320×320的大小，它是对比度低，和更为模糊的一些细节，特别的，外围和其他细节很不明显，视觉效果差，使用文中提出的方法部分，克服了以上的一些问题，并得到令人满意的图像效果，如图5（b）显示，该视觉效果得到明显改善。从直方图看来，图像的强度分布的范围是比较均匀，光明与黑暗的灰色区域的分布更合理了。混合遗传算法自动确定函数曲线的非线性变换，从曲线可以得出值9.837,5.7912，它符合图3的C级，跨越压缩变换的中间区域，这与直方图相一致，整体的原始图像低对比度，在中间区域两端压缩拉伸区域与人的视觉一致，增强效果明显提高。

图3，原始图像的大小320×25，整体强度低，使用文中提出的方法得到b图像，我们可以看到地上，椅子和衣服和其他细节的分辨率和对比度比原始图像有明显改善，原始图像的灰度分布集中在较低的区域，其增强的灰度图像的灰度均匀，图3（a）之前和之后基本的变换和非线性变换是一样的，即，图像暗区伸展的值是5.9409, 9.5704，非线性变换的图像退化类型推断是正确的，增强视觉效果和良好的图像增强效应。

图像还没有一个统一的评价标准则很难评价图像质量的提高，有共同峰值信号噪声比（PSNR）方面的评价，但峰值信噪比不反映人类视觉系统误差。因此，我们利用边缘保护指数与对比增长指数评价实验结果。

edgel保护指数（EPI）的定义如下（7）：

??ID?i,j??ID?i?1,j??ID?i,j??ID?i,j?1???i,j??Z (7)

EPI???IO?i,j??IO?i?1,j??IO?i,j??IO?i,j?1?? 对比度增加指数（CII）定义如下：

E?G?GminCD,C?maxCOGmax?Gmin (8)

在图4中，我们比较了小波变换算法得到评估表 TABLE I。

a) 原始图像 b) 通过小波变换的 c) 通过算法增强图像

图像增强

图4 不同工艺的比较 表1 两种方法的比较

图4（a，c）显示原始图像和差分进化算法增强的结果，可以看出，对比度明显提高，更清晰的图像细节，边缘特征更为突出。（B，C）表明，基于小波变换的混合遗传算法的图像比较增强：基于小波变换的增强方法，以提高图像细节部分的视觉效果是在原始图像的改进，但增强不明显；基于自适应混合遗传算法变换的图像增强效果非常好，图像细节，纹理，清晰的结果相比提高了，基于小波变换，大大提高了图像分析后处理的能力。增强实验利用小波变换“sym4”小波，增强差分进化算法实验，它的参数和值分别为5.9409,9.5704。对于一个256×256大小的图像变换的自适应混合遗传算法在MATLAB 7软件中的图像增强，计算时间约为2秒，操作很快。从表一中客观的评价标准可以看出，无论是从边缘保护指数，或以提高对比度指数，基于自适应混合遗传算法相比传统的小波变换方法具有较大的增强，这是本节介绍的方法的客观优势。

从以上分析，我们可以看到，这种方法是有用的和有效的。

Ⅵ.结论

在本文中，为了保持完整性的视角的图像信息，利用混合遗传算法来进行图像

增强，从实验结果可以看出，基于混合遗传算法的图像增强方法具有明显的效果。与其他进化算法相比，该算法的混合遗传算法突出表现在它是简单的，鲁棒性和快速收敛，在每次运行时发现它几乎是最佳的解决方案，该混合遗传算法只有几个参数需要设置和相同的一组参数可以用在许多不同的问题。应用混合遗传算法的快速搜索能力对于一个给定的测试图像的自适应变异进行搜索，最终确定变换函数的最佳参数值。与穷举法相比，显着减少时间求解，解决了计算的复杂性。因此，所提出的这个图像增强方法具有一定的实用价值。

参考文献

[1] HE Binetal., Visual C++ Digital Image Processing [M], Posts & Telecom Press, 2001,4:473～477

[2] Storn R, Price K. Differential Evolution—a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Space[R]. International Computer Science Institute, Berlaey, 1995.

[3] Tubbs J D. A note on parametric image enhancement [J].Pattern Recognition.1997, 30(6):617-621.

[4] TANG Ming, MA Song De, XIAO Jing. Enhancing Far Infrared Image Sequences with Model Based Adaptive Filtering [J] . CHINESE JOURNAL OF COMPUTERS, 2000, 23(8):893-896.

[5] ZHOU Ji Liu, LV Hang, Image Enhancement Based on A New Genetic Algorithm [J]. Chinese Journal of Computers, 2001, 24(9):959-964.

[6] LI Yun, LIU Xuecheng. On Algorithm of Image Constract Enhancement Based on Wavelet Transformation [J]. Computer Applications and Software, 2008,8.

[7] XIE Mei-hua, WANG Zheng-ming, The Partial Differential Equation Method for Image Resolution Enhancement [J]. Journal of Remote Sensing, 2005，9(6):673-679.

Application Of Digital Image Processing In The Measurement Of

Casting Surface Roughness

Tian Xiaojing, Wang Xiaoyu, Wang Longji Dalian Jiaotong University, Liaoning, CHN, tzy@djtu.edu.cn

Dong Huajun 1 Dalian Jiaotong University, Liaoning, CHN, tzy@djtu.edu.cn 2.CEET Pinggao Group Company Limited, Henan, CHN, huajundong4025@163.com

Ahstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parameters

and the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.

Keywords-casting surface; roughness measurement; image processing; feature parameters

Ⅰ.INTRODUCTION

Nowadays the demand for the quality and surface roughness of machining is highly

increased, and the machine vision inspection based on image processing has become one of the hotspot of measuring technology in mechanical industry due to their advantages such as non-contact, fast speed, suitable precision, strong ability of anti-interference, etc [1,2]. As there is no laws about the casting surface and the range of roughness is wide, detection parameters just related to highly direction can not meet the current requirements of the development of the photoelectric technology, horizontal spacing or roughness also requires a quantitative representation. Therefore, the three-dimensional evaluation system of the casting surface roughness is established as the goal [3,4], surface roughness measurement based on image processing technology is presented. Image preprocessing is deduced through the image enhancement processing, the image binary conversation. The three-dimensional roughness evaluation based on the feature parameters is performed . An automatic detection interface of casting surface roughness based on MA TLAB is compiled which provides a solid foundation for the online and fast detection of casting surface roughness.

II. CASTING SURFACE IMAGE ACQUISITION SYSTEM

The acquisition system is composed of the sample carrier, microscope, CCD camera, image acquisition card and the computer. Sample carrier is used to place tested castings. According to the experimental requirements, we can select a fixed carrier and the sample location can be manually transformed, or select curing specimens and the position of the sampling stage can be changed. Figure 1 shows the whole processing procedure.,Firstly,the detected castings should be placed in the illuminated backgrounds as far as possible, and then through regulating optical lens, setting the CCD camera resolution and exposure time, the pictures collected by CCD are saved to computer memory through the acquisition card. The image preprocessing and feature value extraction on casting surface based on corresponding software are followed. Finally the detecting result is output.

III. CASTING SURFACE IMAGE PROCESSING

Casting surface image processing includes image editing, equalization processing, image enhancement and the image binary conversation,etc. The original and clipped images of the measured casting is given in Figure 2. In which a) presents the original image and b) shows the clipped image.

A. Image Enhancement

Image enhancement is a kind of processing method which can highlight certain image information according to some specific needs and weaken or remove some unwanted informations at the same time[5].In order to obtain more clearly contour of the casting surface equalization processing of the image namely the correction of the image histogram should be pre-processed before image segmentation processing. Figure 3 shows the original grayscale image and equalization processing image and their histograms. As shown in the figure, each gray level of the histogram has substantially the same pixel point and becomes more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced.

Fig.2 Casting surface image

Fig.3 Equalization processing image

B. Image Segmentation

Image segmentation is the process of pixel classification in essence. It is a very important technology by threshold classification. The optimal threshold is attained through the instmction thresh = graythresh (II). Figure 4 shows the image of the binary

conversation. The gray value of the black areas of the Image displays the portion of the contour less than the threshold (0.43137), while the white area shows the gray value greater than the threshold. The shadows and shading emerge in the bright region may be caused by noise or surface depression.

Fig4 Binary conversation

IV. ROUGHNESS PARAMETER EXTRACTION

In order to detect the surface roughness, it is necessary to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surface's peak area is parameter that can reflect the roughness of horizontal workpiece.And kurtosis parameter can both characterize the roughness of vertical direction and horizontal direction. Therefore, this paper establishes histogram of the mean and variance, the unit surface's peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is compiled based on MATLAB. Figure 5 shows the detection interface of surface roughness. Image preprocessing of the clipped casting can be successfully achieved by this software, which includes image filtering, image enhancement, image segmentation and histogram equalization, and it can also display the extracted evaluation parameters of surface roughness.

Fig.5 Automatic roughness measurement interface

V. CONCLUSIONS

This paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is composed of image acquisition, image enhancement, the image binary conversation and the extraction of characteristic parameters of roughness casting surface. The interface of image preprocessing and the extraction of roughness evaluation parameters is compiled by MA TLAB which can provide a solid foundation for the online and fast detection of casting surface roughness.

REFERENCE

[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction [1]. Optical instruments 1996, 18 (1): 32-37.

[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin: Harbin University of Science and Technology

[3] BRADLEY C. Automated surface roughness measurement[1]. The International Journal of Advanced Manufacturing Technology ,2000,16(9) :668-674.

[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method [J]. Aerospace measurement technology, 2000, 20(4): 2-10.

[5] Liu He. Digital image processing and application [ M]. China Electric Power Press, 2005

数字图像处理在铸件表面粗糙度测量中的应用

Tian Xiaojing, Wang Xiaoyu, Wang Longji 大连交通大学，辽宁，中国，tzy@djtu.edu.cn

Dong Huajun 1大连交通大学，辽宁，中国，tzy@djtu.edu.cn

2.CEET平高集团有限公司，河南，中国， huajundong4025@163.com

摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。由CCD获得的图像的步骤是通过预先处理图像编辑，图像均衡，图像二进制对话和特征参数的提取，实现铸件表面粗糙度测量。三维评价方法是得到评价参数和铸件表面粗糙度的特征参数的提取。一种基于MA TLAB的铸造表面粗糙度自动检测接口程序，可以提供一个坚实的基础在线和快速的基于图像处理技术的铸造表面粗糙度检测。

关键词—铸造表面粗糙度测量；图像处理；特征参数

Ⅰ.介绍

如今在质量和加工表面粗糙度的高度增加的需求下，由于如非接触，热点速度快，适用于精度高，抗干扰能力强等的优点，基于图像处理的机器视觉检测已成为机械工业中主要测量技术之一[1,2]。由于没有规定和限制，铸件表面粗糙度的范围是广泛的，检测参数与高度方向光电技术的发展，不能满足目前的要求，水平间距或粗糙度也需要一个定量表示。因此，基于图像处理技术的表面粗糙度测量方法，对铸造表面粗糙度建立三维评价体系为目标[ 3,4 ]。通过图像增强处理，推导出图像的预处理和图像二值谈话。三维粗糙度是基于特征参数进行评价的。一种基于MA TLAB的铸造表面粗糙度自动检测界面的编制提供了坚实的在线快速铸造表面粗糙度检测。

Ⅱ.铸件表面图像采集系统

采集系统由采样载体，显微镜，CCD摄像头，图像采集卡和计算机组成。样品载体是用来测试铸件。根据实验要求，我们可以选择一个固定的载体，采样位置可以手动转换，选择固化试样与采样阶段的位置是可以改变的。图1显示了整个加工过程，首先，检测到铸件应尽可能放置在明亮的背景下，然后通过调节光学透镜，设置CCD摄像机分辨率和曝光时间，对CCD采集到的图片通过采集卡保存到计算机内存。根据相应的软件对铸件表面进行图像预处理和特征值提取，最后检测结果输出。

图1 铸造图像采集系统

Ⅲ.铸件表面图像处理

铸件表面图像处理主要包括图像编辑，均衡处理，图像增强和图像二值谈话等。原始的图像测量铸件图2中给出。其中（a）显示了原始图像和（b)显示剪辑图像。

A.图像增强

图像增强是一种处理方法，可以突出某些图像信息,根据特定的需要同时可以削弱或删除一些不必要的信息[5]。为了获得更清楚轮廓的铸件表面均匀化处理的图像即校正图像的直方图应在图像分割处理前预先处理。图3显示了原始灰度图像及其直方图均衡化处理的图像。如图所示，每个灰度级的直方图具有基本相同的像素点，灰度均衡化处理后变得更加平。校正后的对比度增强的图像将变得更加清晰。

a) 原始图像 b) 修剪图像 图2 铸件表面图像

a) 灰度图像 b) 直方图

c)均衡图像 d）均衡直方图

图3 均衡处理图像

B.图像分割

图像分割是在本质上的像素分类的过程。它是由阈值分类的一个非常重要的技术。最优阈值是通过instmction脱粒= graythresh（II）达到的。图4显示图像的二进制谈话。图中的黑色区域显示部分的轮廓的灰度值低于阈值（0.43137），而白色区域表示灰度值大于阈值。阴影和阴影在明亮的区域出现可能造成噪音或表面凹陷。

a) 灰度图像 b) 二值图像 图4 图像的二值化

Ⅳ.粗糙度参数提取

为了检测表面粗糙度，需要提取粗糙度特征参数。平均直方图和方差是用来描

述表面轮廓纹理尺寸参数。而单位表面的峰面积参数能反映工件的粗糙度水平。峰度参数可以表征垂直方向和水平方向的粗糙度。因此，本文建立直方图的均值和方差，单位表面的峰面积和陡度作为粗糙度评价参数的铸件三维评价。图像预处理和特征提取的界面是基于MATLAB编制的。图5显示了表面粗糙度的检测接口。图像预处理通过这个软件成功地实现了可裁剪的铸造，其中包括图像滤波，图像增强，图像分割和直方图均衡化，而且还可以显示所提取的评价表面粗糙度参数。

图5自动粗糙度测量接口

V.结论

本文研究了铸件表面粗糙度测量方法的基础上的数字图像处理技术。该方法由图像采集，图像增强，图像二值的对话和铸件表面的粗糙度特征参数的提取组成。MA TLAB编译图像预处理和提取粗糙度评估参数的接口，它可以提供铸件表面粗糙度的在线和快速检测一个坚实的基础。

参考文献

[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction [1]. Optical instruments 1996, 18 (1): 32-37.

[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin: Harbin University of Science and Technology

[3] BRADLEY C. Automated surface roughness measurement[1]. The International Journal of Advanced Manufacturing Technology ,2000,16(9) :668-674.

[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method [J]. Aerospace measurement technology, 2000, 20(4): 2-10.

[5] Liu He. Digital image processing and application [ M]. China Electric Power Press, 2005

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