1、安徽工业大学 毕业设计（外文翻译）说明书装订线第 1 页 共 23 页Hybrid Genetic Algorithm Based Image EnhancementTechnologyMu Dongzhou Department of the Information Engineering XuZhou College of Industrial TechnologyXuZhou, China Xu Chao and Ge Hongmei Department of the Information Engineering XuZhou College of Industrial Techno
2、logyXuZhou, China xuch , Abstractin 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.
3、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
4、 effectiveness of the method.Keywords- Image enhancement; Hybrid Genetic Algorithm; adaptive enhancementI. INTRODUCTIONIn 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
5、 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 prop
6、osed 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 t
7、he 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 charac
8、teristics 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.安徽工业大学 毕业
9、设计（外文翻译）说明书装订线第 2 页 共 23 页II. IMAGE ENHANCEMENT TECHNOLOGYImage 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 i
10、mage 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
11、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
12、 filtering, multi-scale multi-resolution image enhancement applied 1.III. DIFFERENTIAL EVOLUTION ALGORITHMDifferential 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
13、, 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
14、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
15、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):i=1,.,P (2)321rri XFXV, , are three randomly selected individuals from group, and r1 r2 r3 i.F 1rX23r is a range of 0, 2
16、 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
17、automatically reduced.DE algorithm selection operation is a “greedy “ selection mode, if and only if the new 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 t
18、he original group, once again as the next generation of the parent vector.安徽工业大学 毕业设计（外文翻译）说明书装订线第 3 页 共 23 页IV. HYBRID GA FOR IMAGE ENHANCEMENT IMAGEenhancement is the foundation to get the fast object detection, so it is necessary to find real-time and good performance algorithm. For the practical
19、 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 solv
20、ed. 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
21、 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
22、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
23、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):(4) where:10,1,01 dttBufu(5)01,dttBFor different value of and , we can get response curve from (4) and (5).The hybrid GA can make use of
24、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 foll
25、ows:Assuming the original image pixel (x, y) of the pixel gray level by the formula (4), denoted by , , here is the image domain. Enhanced image is denoted by Ixy. xyiFirstly, the image gray value normalized into 0, 1 by (6).(6)minaxigywhere: and express the maximum and minimum of image gray relativ
26、ely.maxiinDefine the nonlinear transformation function f(u) (0u1) to transform source image to Gxy=f( ), where the 0 Gxy 1.xyg安徽工业大学 毕业设计（外文翻译）说明书装订线第 4 页 共 23 页Finally, we use the hybrid genetic algorithm to determine the appropriate Beta function f (u) the optimal parameters and . Will enhance the
27、 image Gxy transformed antinormalized.V. EXPERIMENT AND ANALYSISIn 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 differen
28、t types of degraded image.Figure 2, the size of the original image a 320 320, its 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
29、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 ge
30、netic algorithm to automatically identify the 安徽工业大学 毕业设计（外文翻译）说明书装订线第 5 页 共 23 页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 transf
31、orm 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,
32、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
33、 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 correc
34、t, 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
35、 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:安徽工业大学 毕业设计（外文翻译）说明书装订线第 6 页 共 23 页(7)Contrast Increase Index (CII) is defined as follows:(8) minax,GCEODIn figure 4, we com
36、pared 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.
37、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
38、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 “sym4“ w
39、avelet, 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
40、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
41、 advantages of the method. From above analysis, we can see 安徽工业大学 毕业设计（外文翻译）说明书装订线第 7 页 共 23 页that this method.From above analysis, we can see that this method can be useful and effective.VI. CONCLUSIONIn this paper, to maintain the integrity of the perspective image information, the use of Hybrid g
42、enetic 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, robu
43、st 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
44、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.REFE
45、RENCES1 HE Bin et al., Visual C+ Digital Image Processing M, Posts & Telecom Press, 2001,4:4734772 Storn R, Price K. Differential Evolutiona Simple and Efficient Adaptive Scheme for Global Optimization over Continuous SpaceR. International Computer Science Institute, Berlaey, 1995.3 Tubbs J D. A not
46、e 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 Ge
47、netic 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.安徽工业大学 毕业设计（外文翻译）说明书装订线第 8 页 共 23 页基于混合遗传算法的图像增强技术Mu Dongzhou 徐州工业职业技术学院信息工程系 XuZhou, Chinamudzhm
