數(shù)字圖像處理英文文獻(xiàn)翻譯參考

1、裝訂線安徽工業(yè)大學(xué) 畢業(yè)設(shè)計（外文翻譯）說明書hybrid genetic algorithm based image enhancementtechnologymu dongzhou department of the information engineering xuzhou college of industrial technologyxuzhou, china mudzhxu chao and ge hongmei department of the information engineering xuzhou college of industrial technologyxuz

2、hou, china xuch , gehmabstractin 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

3、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 ef

4、fectiveness 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 ge

5、t 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 propose

6、d 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

7、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 character

8、istics 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 en

9、hancement 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 image data, but will choose the appropr

10、iate 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.

11、 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-resoluti

12、on 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, easy to understand. de algorithm is

13、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 f

14、itness 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

15、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 between the actual type constant factor difference vector i

16、s 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

17、"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 original group, once again as the next generati

18、on 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 algorithms need to determine the parameters

19、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 f

20、or 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

21、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 u

22、se 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

23、 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 to search for the best function to determ

24、ine 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 by, her

25、e 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 source image to gxy=f(), where the 0 gxy 1.f

26、inally, 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-scale images degraded; the program performe

27、d 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 partic

28、ular, 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

29、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 cu

30、rve 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 hum

31、an 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 c

32、ontrast 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

33、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 sign

34、al 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: (

35、7) contrast increase index (cii) is defined as follows: (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 c

36、ontrast 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

37、 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 processin

38、g helpful. experimental enhancement experiment using wavelet transform "sym4" 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

39、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 base

40、d 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. conclusionin this paper, to maintain the integr

41、ity 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

42、 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 hybri

43、d 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 pr

44、oposed image enhancement method has some practical value.references1 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. internati

45、onal 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):89

46、3-896.5 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

47、zheng-ming, the partial differential equation method for image resolution enhancement j. journal of remote sensing, 2005，9(6):673-679.基于混合遺傳算法的圖像增強技術(shù)mu dongzhou 徐州工業(yè)職業(yè)技術(shù)學(xué)院信息工程系 xuzhou, chinamudzhxu chao and ge hongmei 徐州工業(yè)職業(yè)技術(shù)學(xué)院信息工程系 xuzhou, china xuch , gehm摘要在圖像增強之中，塔布斯提出了歸一化不完全函數(shù)表示常用的幾種使用的非線性變換函數(shù)

48、對圖像進(jìn)行研究增強。但如何確定beta系數(shù)功能仍然是一個問題。在圖像增強處理和利用遺傳算法快速算法的搜索能力進(jìn)行自適應(yīng)變異和搜索我們提出了一種混合遺傳將微分進(jìn)化算法。最后利用仿真實驗證明了該方法的有效性。關(guān)鍵詞圖像增強；混合遺傳算法；自適應(yīng)增強.介紹在圖像形成，傳遞或轉(zhuǎn)換過程，由于其他客觀因素，如系統(tǒng)噪聲，不足或過度曝光，相對運動等的影響會使圖像通常與原始圖像之間有差別（簡稱退化或退化）。退化圖像通常模糊或信息的提取通過機器后減少甚至是錯誤的，它必須采取一些改進(jìn)措施。圖像增強技術(shù)是在其目的是為了提高圖像的質(zhì)量這個意義上提出的。模糊圖像增強情況是根據(jù)圖像使用各種特殊技術(shù)集錦的一些信息圖像，減少或

49、消除不相關(guān)的信息，來強調(diào)整體或局部特征的目標(biāo)圖像。圖像增強方法仍沒有統(tǒng)一的理論，圖像增強技術(shù)可分為三類別：點運算，與空間頻率增強方法增強法。本文介紹了根據(jù)圖像特征自動調(diào)整自適應(yīng)圖像增強方法，稱為混合遺傳算法。為了實現(xiàn)圖像的自適應(yīng)增強它結(jié)合了差分進(jìn)化自適應(yīng)搜索算法，自動確定的參數(shù)值的變換函數(shù)。.圖像增強技術(shù)圖像增強是圖像的某些特征，如輪廓，對比，強調(diào)或突出的邊緣等為了便于檢測和進(jìn)一步的分析和處理. 增強將不會增加圖像中的信息數(shù)據(jù)，但會選擇適當(dāng)?shù)膭討B(tài)范圍的功能的擴展，使得這些特點更容易檢測或確定，為后續(xù)的分析和處理的檢測打下良好的基礎(chǔ)。圖像增強方法包括點運算，空間濾波，頻域濾波類別。點運算包括對比

50、度拉伸，直方圖建模，并限制噪聲和圖像減影技術(shù)?？臻g濾波器包括低通濾波，中值濾波，高通濾波器（銳化）。頻率濾波器包括同態(tài)濾波，多尺度多分辨率圖像增強中的應(yīng)用1。.差分進(jìn)化算法差分進(jìn)化（de）首次提出了強硬的價值，并與其他進(jìn)化算法進(jìn)行比較，de算法具有強大的空間搜索能力，易實現(xiàn)，容易理解。de算法是一種新型的搜索算法，它首先是在搜索空間中隨機產(chǎn)生初始種群，然后計算之間的任何差異向量的兩個成員，所不同的添加到向量的第三個成員，通過該方法，形成一個新的個人。如果你發(fā)現(xiàn)新的個體成員比原來的好，然后替換原來的個體，自我的形成。de操作作為遺傳算法一樣，它結(jié)論突變，交叉和選擇，但方法是不同的。我們假設(shè)組的大

51、小是p，矢量維d，我們可以表達(dá)的目標(biāo)向量為（1）： xi=xi1，xi2，xid (i =1,p) (1)變異向量可以表示為（2）： i=1,.,p (2),是三個從群中隨機選擇的個人 ，其中，r1r2r3i。f是一系列的 0，2 之間的實際類型的用于控制影響的常數(shù)因子差異向量，通常被稱為比例因子。 顯然，矢量之間的區(qū)別越小則干擾也越小，這意味著如果組接近最佳值，擾動會自動降低。 de算法的選擇操作是一個“貪婪”的選擇模式，當(dāng)且僅當(dāng)新的矢量ui比目標(biāo)向量xi更好更健全，ui將被保留到下一組。否則，目標(biāo)向量xi留在原來的組，再次作為下一代的父矢量。 .圖像增強圖像的混合遺傳算法 增強是獲得快速對

52、象檢測的基礎(chǔ)，因此有必要尋找實時性能好的算法。對不同系統(tǒng)的實際要求，許多算法需要確定的參數(shù)和人工閾值。它可以使用一個非完全beta函數(shù)來完全覆蓋典型變換式的圖像增強，但確定beta函數(shù)參數(shù)仍有許多亟待解決的問題。本節(jié)介紹了一種beta功能，因為根據(jù)適用的圖像增強的方法，自適應(yīng)混合遺傳算法的搜索的能力，自動確定變換命令的參數(shù)值來實現(xiàn)圖像增強的自適應(yīng)功能。 圖像增強的目的是提高圖像質(zhì)量，是在指定的比較突出的特點恢復(fù)退化圖像細(xì)節(jié)等。一個共同的特征的退化圖像通常是對比的下側(cè)呈明亮的，暗淡或灰色濃。低對比度退化圖像可拉伸達(dá)到一種動態(tài)的直方圖增強，如灰度變化。我們用 ixy來說明點（x，y）的灰度級它可以

53、是由（3）表示。 ixy=f(x,y) (3)其中：“f”為一個線性或非線性函數(shù)。在一般情況下，灰圖像有四個非線性的翻譯6 7，可以是如圖1所示。我們采用歸一化的 beta函數(shù)自動適應(yīng)4類圖像增強轉(zhuǎn)變曲線。（4）中定義： (4)其中： (5)對于不同的，值，我們可以從（4）及（5）中得到響應(yīng)曲線。 圖1 四種傳統(tǒng)的翻譯該混合算法可以利用前面的部分自適應(yīng)差分進(jìn)化算法搜索最佳函數(shù)來確定的值，然后每個像素灰度值為函數(shù)，相應(yīng)的圖1轉(zhuǎn)化，產(chǎn)生理想的圖像增強。詳細(xì)描述如下：假設(shè)原始圖像的像素（x，y）的像素的灰度水平，表示為式（4），記為，這里是圖像域。增強的圖像由ixy表示。首先，圖像的灰度值在（6）中

54、歸到0，1。 (6)其中：imax和imin表示圖像灰度的最大值和最小值。定義非線性變換函數(shù)f（u）（0u1）變換成源圖像gxy=f（gxy），其中，0gxy1。最后，我們使用了混合遺傳算法來確定適當(dāng)?shù)腷eta函數(shù)f（u）的最佳參數(shù)和。 v.實驗和分析在模擬中，我們使用兩種不同類型的灰度圖像退化；程序執(zhí)行了50次，人口大小為30，進(jìn)化600次。結(jié)果表明，提出的方法可以非常有效地提高不同退化圖像類型。 a) 原始圖像 b) 增強圖像 圖2 單個圖像增強過程 a) 原始圖像 b) 增強圖像 圖3 移動對象增強過程圖2，原始圖像為320×320的大小，它是對比度低，和更為模糊的一些細(xì)節(jié)，特

55、別的，外圍和其他細(xì)節(jié)很不明顯，視覺效果差，使用文中提出的方法部分，克服了以上的一些問題，并得到令人滿意的圖像效果，如圖5（b）顯示，該視覺效果得到明顯改善。從直方圖看來，圖像的強度分布的范圍是比較均勻，光明與黑暗的灰色區(qū)域的分布更合理了?；旌线z傳算法自動確定函數(shù)曲線的非線性變換，從曲線可以得出值9.837,5.7912，它符合圖3的c級，跨越壓縮變換的中間區(qū)域，這與直方圖相一致，整體的原始圖像低對比度，在中間區(qū)域兩端壓縮拉伸區(qū)域與人的視覺一致，增強效果明顯提高。圖3，原始圖像的大小320×25，整體強度低，使用文中提出的方法得到b圖像，我們可以看到地上，椅子和衣服和其他細(xì)節(jié)的分辨率和對比度比原始圖像有明顯改善，原始圖像的灰度分布集中在較低的區(qū)域，其增強的灰度圖像的灰度均勻，圖3（a）之前和之后基本的變換和非線性變換是一樣的，即，圖像暗區(qū)伸展的值是5.9409, 9.5704，非線性變換的圖像退化類型推斷是正確的，增強視覺效果和良好的圖像增強效應(yīng)。圖像還沒有一個統(tǒng)一的評價標(biāo)準(zhǔn)則很難評價圖像質(zhì)量的提高，有共同峰值信號噪聲比（psnr）方面的評價，但峰值信噪比不反映人類視覺系統(tǒng)誤差。因此，我們利用邊緣保護