1、数字图像处理英文文献翻译参考0000Hybrid Genetic Algorithm Based Image EnhancementTechnologyAbstract: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 co
2、efficients 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. F
3、inally we use the Simulation experiment to prove the 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 excessi
4、ve 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 i
5、ts 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 in
6、formation, 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
7、 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 or
8、der to achieve adaptive image enhancement.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 info
9、rmation 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
10、 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 includ
11、ing homomorphism 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 e
12、asy 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 M
13、ethod 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 ar
14、e 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): i=1,.,P (2),are three randomly selected individuals from group, and r1r2r3i.F is a range of 0, 2 betwe
15、en 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 automa
16、tically 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 the origin
17、al group, once again as the next generation of the parent vector.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 requirements of different systems, many alg
18、orithms 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, s
19、ince 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 quali
20、ty, 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 enh
21、ancement, 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 inco
22、mplete Beta function to automatically fit the 4 categories of image enhancement transformation curve. It defines in (4): (4) where: (5)For different value of and , we can get response curve from (4) and (5).The hybrid GA can make use of the previous section adaptive differential evolution algorithm
23、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
24、level by the formula (4), denoted by, here is the image domain. Enhanced image is denoted by Ixy. Firstly, the image gray value normalized into 0, 1 by (6). (6)where: and express the maximum and minimum of image gray relatively.Define the nonlinear transformation function f(u) (0u1) to transform sou
25、rce image to Gxy=f(), where the 0 Gxy 1.Finally, we use the hybrid genetic algorithm to determine the appropriate Beta function f (u) the optimal parameters and . Will enhance the image Gxy transformed antinormalized.V. EXPERIMENT AND ANALYSISIn the simulation, we used two different types of gray-sc
26、ale 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, its the contrast to low, and some details o
27、f 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
28、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 bef
29、ore 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
30、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 o
31、f 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,
32、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 eval
