Journal of Engineering Science and Technology Review 10 (3) (2017) Research Article

Transcription

1 Jestr Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 Research Article Sel-Adaptive Anisotropic Image Enhancement Algorithm Based on Local Variance Yan Chen,, Yunjiao Bai, Quan Zhang, Yanling Wang and Zhiguo Gui,* School o Inormation and Communication Engineering, North Universit o China, Taiuan 03005, China School o Engineering, Southern Illinois Universit, Edwardsville, Illinois, 606, USA JOURNAL OF Engineering Science and Technolog Review Received 4 Februar 07; Accepted June 07 Abstract When an image is enhanced using the traditional Laplacian enhancement model, the enhancement eect is evident but the overshoot phenomenon occurs simultaneousl as the neighborhood and weight increase. To solve the contradiction between edge preservation and noise suppression during the image enhancement process, this stud proposed an improved partial dierential equation image enhancement algorithm. The algorithm combined the neighborhood eatures o digital images, mined the relationship between the local variance and the detail inormation o images, and classiied the image noise and detail inormation with local variance. On this basis, the periodic change eatures o the trigonometric unction were used to construct anisotropic diusion coeicients, the numerical solution method o the partial dierential equation was used to calculate the numerical solution o the proposed algorithm, and simulation experiment was implemented to analze the inluence o the algorithm model parameters on enhancement eect. Lastl, the anisotropic enhancement algorithm proposed in this stud was validated through comparison with the traditional Laplace algorithm. Results indicate that tangential direction parameter and the number o iterations exert inluence on the image enhancement eects. That is, when tangential direction parameter is set as 0.5 and the iterative operation is conducted 5 times, the algorithm smoothens the noise and enhances the image details. When the proposed algorithm is used to process low-contrast industrial image, the signal-to-noise ratio o the enhanced image is increased b 8 db and the mean squared error is reduced b 55% compared with the enhancement eect o the traditional Laplace enhancement method. In terms o subjective visual eect, the noise o the enhanced image is iltered, whereas detailed inormation (e.g., numerous weak edges) is reserved. Hence, subjective and objective evaluations relected that the proposed algorithm can eectivel enhance image details and avoid over-enhancement. Moreover, the proposed algorithm is o avorable adaptabilit and certain reerence value to low-contrast image enhancement. Kewords: Image Enhancement, Anisotropic Diusion, Laplace. Introduction Digital image is inluenced b illumination or all tpes o noise sources during the imaging process; thus, the generated image has blurred edge and reduced eature inormation. Accordingl, image smoothing should be perormed prior to the ollow-up processing. Gaussian iltering is the most common image iltering method among the traditional image smoothing methods. However, Gaussian iltering belongs to lowpass iltering, which can process the edge and non-edge o an image in the same iltering manner, thereb blurring the image eatures and resulting in eature loss. Hence, scholars have been developing a diusion method that can dierentiate the eature inormation points rom the noise points o images. This method can adopt dierent iltering processes or boundar and lat regions. In recent ears, a ew scholars have studied image processing methods based on partial dierential equation []. In particular, integer-order partial dierential is used b image enhancement technolog based on integer order partial dierential to detect image gradient * address: nucchenan@6.com ISSN: Eastern Macedonia and Thrace Institute o Technolog. All rights reserved. doi:0.503/jestr value. Moreover, the enhancement processing o an image with edge sharpening is realized through dierential iltering. The Robert operator is a gradient operator based on irstorder dierential. The algorithm has avorable lit eect in edges but it is sensitive to noise []; thus, it cannot enhance the texture region. The Prewitt operator [3], which is a irstorder average operator, has certain smoothing unction on image noise. Accordingl, the edge o the processed image is thick and bright, but the image texture is suppressed and there is a pseudo edge. The Sobel operator is improved based on the Prewitt operator, the pixel is weighted b pixel position. The weight is inversel proportional to the distance rom the center point o the pixel. Hence, the enhanced image has substantiall clear and bright edges. However, the Sobel operator [4] inhibits texture details; thus, it still needs improvement. The Laplace operator is an image-sharpening operator based on second-order dierential. The Laplace equation is a classical partial dierential enhancement method o images. When image enhancement is perormed using the Laplace isotropic operator, given that highrequenc components are excessivel enhanced, the image presents a considerabl cluttered eect called the explosion phenomenon [5]. The detection o the image edges using the Gaussian Laplace operator [6] can relect the visual characteristics o the human ees. In practical application, noise has immense inluence on an image

2 Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 because the scale actor cannot make sel-adaptive adjustment. Based on the above background, this stud presented a sel-adaptive diused anisotropic image enhancement method, which could deal with image noise and edge, enhance image detail inormation and improve image contrast.. State o the art At present, image enhancement methods are mainl divided into the spatial domain and transorm domain methods. The spatial domain method directl processes pixel points, and the representative algorithms include the histogram method, the gra-scale transorm method, the Retinex method, and other enhancement algorithms. The histogram method is one o the simplest and most eective methods, and uniorml distributes pixel points that concentrate in narrow gra-scale interval inside the original image based on gra-scale statistical characteristics. The advantages o histogram method lie in simple but high-speed algorithm that can automaticall enhance images. However, it is sensitive to noise, with eas detail loss and serious distortion occurring in a ew regions. HAN [7] proposed an improved histogram equalization algorithm that could eectivel solve the problems o over-combination o gra scales and easil detail loss o traditional histogram method. However, the color loss phenomenon would easil happen to the color images. The gra-scale transorm method maps relativel centralized low-gra value in the input image into the output gra value in relativel wide interval. The commonl used mapping methods are the logarithmic and the parabolic transormations. This method is o high-processing speed, but parameter adjustment cannot be sel-adaptivel conirmed based on the image eatures. Consequentl, the supersaturation phenomenon will easil happen in a ew image regions. ZHOU et al. [8] proposed a method that simultaneousl enhances global brightness and local contrast. This algorithm eectivel solved the sel-adaption problem o the algorithm in gra-scale transormation; however, the eect o this method on images with less brightness still needs improvement. The Retinex algorithm, which is an image enhancement method o light compensation, reaches an enhancement eect based on the color correction o the gra-scale assumption and can simultaneousl realize the enhancement o the global and local contrasts o the image. Through the simulation o the human vision sstem, the Retinex algorithm obtains continuous development. Given that the essence o the Retinex algorithm is based on grascale assumption, uniorm color region violates gra-scale assumption theor. Thus, the image color ater processing is unsaturated and becomes gra tone with substantial color distortion problem. XIAO et al. [9] proposed an improved multi-scale Retinex algorithm with color restoration. This algorithm could eectivel solve such problems as color distortion and noise ampliication. However, this algorithm is highl complex; thus, it cannot be used in real-time processing. The transorm domain method transorms an image into another spatial domain or processing. The more relativel classical transorm domain method is the wavelet transorm method. The wavelet coeicients are acquired b doing wavelet transorm o an image. Thereater, the coeicients o the dierent requencies within the image are multiplied b the dierent coeicients to enhance or inhibit the eects 65 o a ew components. Image processing technolog [0] based on wavelet transorm has drawn the considerable attention o scholars because wavelet transorm maniests the time-domain eatures o an image and embodies its requenc-domain eatures. That is, this technolog extracts image edges and the overall structure. This characteristic agrees with the low illumination image enhancement technique. However, this method primaril needs to deine wavelet basis; thus, the application o the algorithm is limited. Since Perona and Malik proposed the anisotropic diusion model (i.e. Perona Malik (P M) model) [] in 990, partial dierential equation has developed rapidl in the ield o image processing. When the partial dierential equation is used to enhance tan image, this equation can directl process signiicant geometric eatures, such as gradient, tangential line, and level set, and can eectivel simulate the diusion characteristics o the human vision. In addition, the numerical analsis method o partial dierential equation is accurate and stable. Consequentl, the advantages o the partial dierential equation have drawn substantial attention rom a group o local and international experts. Thereater, the improved denoising models have been developed in succession. Catte proposed a Gaussian regularization model [], which ielded avorable eects when applied to edge detection and image restoration. The variable index model proposed b Guo [3] could seladaptivel control diusion pattern. P-M diusion and Gaussian smoothing were conducted based on dierent image eatures. Compared with the traditional P M model, the new model eectivel extracted image edge eatures. Ratner used the telegram diusion equation [4] to substantiall reserve the details; hence, it is extensivel applied in image processing. Accordingl, You and Kaveh proposed a ourth-order partial dierential equation [5] and Bai and Feng introduced a ractional-order partial dierential equation [6], thereb enabling the urther development o the partial dierential equation obtained in the aspect o image denoising. Image enhancement technolog based on partial dierential equation is an important branch o image processing. Moreover, the most tpical one is inverse heat conduction equation, namel the Laplace image enhancement algorithm. This equation conducts isotropic enhancement o the image. Moreover, it can substantiall enhance image eatures but cannot inhibit noise and will easil generate the overshoot phenomenon. An improved algorithm proposed b Wang based on PDE(Partial Dierential Equation)solved the enhancement problem o nonlinear dim target [7]. The dim target image was enhanced b the algorithm b importing the proper gradient threshold values and sharpening intensit parameters. However, the algorithm still had non-ideal enhancement eect because the diusion direction had incomplete sel-adaptabilit. In summar, image enhancement technolog has obtained certain achievements. However, existing algorithms encounter numerous problems, such as eas detail loss, color distortion and high algorithm complexit. Moreover, lowcontrast image enhancement technolog has et to guarantee the perormance o a visual sstem under low-light environment. This stud used the analsis o the partial dierential equation as basis to propose a method that can eectivel improve image contrast. The remainder o this stud is organized as ollows. Section expounds on the main research conditions o the digital image enhancement algorithms. Section 3 proposes

3 Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 the improved algorithm, analzes the problems that should be solved in image enhancement, and particularl proposes a sel-adaptive anisotropic enhancement algorithm that combines local variance and trigonometric unction. Section 4 analzes the inluence o several important parameters on the proposed algorithm, as well as compares the proposed algorithm with the traditional Laplace algorithm. Section 5 summarizes the conclusions. 3. Methodolog 3. Laplace image enhancement algorithm The Laplace image enhancement algorithm is a commonl used image detail enhancement method. Dennis Gabor proposed that inverting the time direction o the heat conduction model could enhance high-requenc components. The temporal evolution pattern o Gabor is as ollows: enhancement eects. To speci the enhancement eects o the dierent masks, three masks are used in this stud to process the image o peppers. Figure is the resulting graph o processing through dierent masks. From the detailed drawing extraction, it can be seen that dierent the Laplace operators are used to extract the detail inormation o the image, and the eature extraction is more obvious with the increase o template weight coeicient. Compared with the original image, the image enhanced using the Laplace algorithm preserves the edge detail eatures in the image while ampliing background noise. The weight coeicient o the third mask is the maximum. Compared with the irst two masks, the third mask can substantiall extract the detailed eatures; however, the image noise becomes considerabl serious with severe overshoot phenomenon. ( x,, t) Δ ( x,, t), t > 0 t ( x,,0) ( x, () (a) Original Image o Peppers For the convenience o solving this equation, the skills o solving standard heat conduction equation can be used: irstorder Talor series expansion. The speciic process is as ollows: spatial variables x and are ixed, (, x,) t is expanded based on the Talor series nearb t 0 about t. The ollowing equation can be obtained: (b) Detailed Extracted B Mask(a) (c) Image Enhanced through Mask(a) ( x,, t) ( x,, t) ( x,,0) t t t 0 ( x, tδ ( x, () This equation is called Laplace algorithm and Δ is the Laplace operator. The Δ operator is isotropic and can be decomposed based on the gradient direction n and tangential direction s orthogonal to n : (d) Details Extracted b Mask(b) (e) Image Enhanced through Mask(b) s Δ (3) Hence, the standard Laplace algorithm can be rewritten into the ollowing orm: t ( x, ( x, t (4) s In practical engineering, the Laplace operator is considerabl convenient. One o masks in the ollowing three orms is oten adopted. () Details Extracted (g) Image Enhanced b Mask(c) through Mask (c) Fig.. Results o the Comparison o the Image Enhancement o Peppers Using the Three Masks The Laplace operator is used to perorm the isotropic enhancement o the image. Accordingl, the algorithm can enhance the details o the image but it also ampliies noise. From our neighborhoods to eight neighborhoods and as weights increases, the enhancement eect becomes substantiall evident. However, severe overshoot phenomenon appeares in the boundar with extreme noise pollution. (a) (b) (c) Fig.. Laplace Operator Mask For the convenience o analzing the Laplace enhancement algorithm, the preceding three masks rom let to right are labeled the mask (a),(b)and (c)(see Fig. ). The dierent masks will obtain images with dierent Sel-adaptive anisotropic enhancement algorithm based on local variance 3.. Basic principles o the algorithm Given that the traditional Laplace image enhancement belongs to the isotropic enhancement algorithm, this algorithm is sensitive to noise, as well as ampliies noise

4 Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 while enhancing the image details. Moreover, the highrequenc components are over-enhanced and the image presents considerabl cluttered eect. In this stud, the tangential and normal diusion coeicients are seladaptivel adjusted as well. Thereater, sel-adaptive anisotropic enhancement algorithm based on the local variance is proposed as ollows: t ( x, ( x, t α β (5) s normal direction is more conducive to detail enhancement and detail protection. where α is the unction o the local variance o the image and β is a positive constant. The core idea o the algorithm is to use the method that combines orward diusion and reverse diusion to enhance the radial image. Given that the gra-scale variance can eectivel dierentiate image details rom noise-containing background [8], the local variance is imported to classi image eatures and construct the sel-adaptive diusion coeicient. The local variance σ ( x, ) o the image within a 3 3 domain at point ( x, ) is deined as ollows: σ ( x, (6) 9 ( ( x i, j) ( x, ) i j where (, x ) is the gra average o the 3 3 window neighborhood. For the convenience o parameter selection, the local variance is normalized to 0-55 in this stud, particularl as ollows: Fig. 3. Change Curves o Function The preceding analsis indicates that T is graduall reduced as the iterations proceed. Moreover, the seladaptive threshold value T is used and deined as ollows: T n T 0 e (9) where n is the number o iterations and T 0 is a positive constant. Fig. 4 shows that the change curve o T as the number o iterations increases. Evidentl, when the number o iterations increased, T is reduced; hence, the details can be substantiall protected. σ ( x, Minσ σ N ( x, 55 (7) Maxσ Minσ where Minσ and Maxσ are the minimum and maximum values, respectivel, o the local variance o the image. Thereore, the change characteristics o the trigonometric unction curve are used to construct the diusion coeicients. T is set as the variance threshold value and T and 4 (55 T) are the ccles to design the cosine unction. The unctionασ ( ) regarding the local variance is constructed to control the normal diusion coeicients as ollows: Fig. 4. Change Curve o T as the Number o Iterations Increases when T 0 0 π cos σ σ T T α ( σ ) (8) π cos ( ) > σ 3 T 50 σ T (55 T ) Fig. 3 shows the change curves o unction T 50 and T 00 ασ ( ) at. When the local variance is within 0 T, ασ ( ) is a positive number and equation (5) is a orward diusion model. When the local variance is above T, ασ ( ) is a negative number and equation (5) is a reverse diusion model. Moreover, as the variance threshold value T is reduced, the region o positive ασ ( ) decreases. So that, the diusion coeicient o ασ ( ) as the 67 ασ ( ) and β are used b the new algorithm model to control the normal and tangential diusion coeicients. The enhancement idea is as ollows.in the tangential direction, the edges and noise need orward diusion and noise should be eliminated, thus, β is a positive constant. In the normal direction, the local variance is within 0 T and α is a positive value, the new model conducts orward diusion and smoothen the noise. WithinT 55, α is a negative value. The new model conducts reverse diusion to enhance the image eatures. A minimum value o - is obtained at the threshold valuet ; hence, the image details are substantiall enhanced. As the local variance increases, α approaches 0 rom, the diusion coeicient at the strong edges is reduced, the edge enhancement is prevented rom becoming too ast, and overshoot occurs when the details are enhanced. Thereater, the overshoot phenomenon appeares during the

5 Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 detail enhancement. Consequentl, when the number n o the iterations increases, the threshold value T o the local variance is graduall reduced. α is decreased in the positive range. This wa, the algorithm initiall centeres on denoising; as the iterations proceeded, such algorithm eventuall centeres on enhancement. The enhancement o the image details is guaranteed, while the noise is prevented rom being enhanced. 3.. Numerical implementation Solving the process o and is as ollows: s The derivative o the D grascale image (, x ) or variables x and is called the image gradient, which is expressed b as ollows: ( x ),, x (0) Thereater, the image gradient modulus is deined as ollows: x () The gradient direction o each point is as ollows: ( x, ) n () The directional derivative along the gradient is as ollows: n 0 On the basis o the preceding equation (3): ( ) n I x, then: (3) (4) xx x s x x x (7) where x,,, xx, and o the image are x calculated through the central dierence scheme as ollows: x xx x i, i i, j i, j i, j j, j i, j i, j i, j 4 i, j i, j i, j i, j i, j 4 i, j (8) 3..3 Algorithm steps This stud uses the preceding principles and analsis as bases to conclude the implementation process o the proposed algorithm as ollows: () The image variance is calculated based on Equation(6), and the normalization processing o the local variance is conducted based on Equation(7). () The initial value T 0 o the threshold value o the local variance is set. Equation(8)is used to calculate the control coeicient ασ ( ) in the normal direction. (3) and are solved based on Equations(5)and s (6). (4) Equation (5) is used to process each point in the image. (5) Steps () to (4) are repeated until a substantiall denoised image is obtained. 4 Result analsis and discussion The speciic experimental veriication was conducted in this section to evaluate the perormance o the sel-adaptive image enhancement algorithm based on the local variance. Moreover, the relevant stud was conducted based mainl on the inluences o the algorithm parameters on the processing eect and comparative stud with other similar algorithms. ( ) x x xx x xx x x, xx x x x x, x xx x x x x x x, x x ( x, ) x (5) (6) (, x) In the s direction, the ollowing equation can be obtained through a similar method: Stud o the main algorithm parameters The proposed algorithm involves the multiple parameters. The initial value T 0 o the threshold value o the local variance, the number n o the iterations and the coeicient β in the tangential direction on the experimental results was mainl analzed in this section. During the speciic experiment, other parameters were set as ixed values, and the processing eects when the dierent values were set or the parameters to be studied were compared to analze the inluence on algorithm perormance. As the main objective depended on veriing the inluences o the algorithm parameters, onl the house image was used as the test image or the experimental data; the time step was t 0..

6 Yan Chen, Yunjiao Bai, Quan Zhang, Yanling Wang and Zhiguo Gui/ Journal o Engineering Science and Technolog Review 0 (3) (07) Inluence o parameter β in tangential direction To analze the inluence o coeicient β in the tangential direction on enhancement eect, Fig.5 shows the result graphs o the algorithm that enhanced the house image under β 0, β 0.5, and β. The initial value o the Fig. 6 shows that when the number o iterations was small, the detail enhancement was weak. B contrast, as the number o iterations increased, the edges were graduall enhanced, but overshoot phenomenon appeared at the edges. To avoid the overshoot phenomenon o the edges, the number o iterations was generall selected as n 5. threshold value was T0 8 and the number o iterations was n 4. The result graphs show that the enhancement eects were dierent when dierent values were considered or β. When β 0, the image details could be enhanced but the image noise was also increased. When β, the noise and details were smoothed; when β 0.5, the details were enhanced, while noise was smoothed. (a) Original House Image (c) β 0.5 (b) 4..3 Inluence o the initial value T0 o threshold value To analze the inluence o the initial value T0 o the threshold value on the enhancement result, the house image was analzed and tested in the experiment. The number o iterations was set as n 4. T0 0, T0 30, and T0 50 were selected to enhance the image. An analsis o Fig. 7 indicated that when T0 0, the edge details o the house were enhanced and the noise was ampliied. When T0 50, the background noise o the house was considerabl inhibited, but the enhancement o the house edges, particularl eaves, was relativel weak. When T0 30, background noise was inhibited while the edge details o the house were enhanced. Thereore, onl when appropriate T0 value was selected would the contradiction between the noise inhibition and the edge enhancement be solved. β 0 (d) β Fig.5. Result Graphs o the House Image Enhancement under Dierent β (a) Original House Image (b) T Inluence o the number n o iterations To analze the inluences o the dierent parameters n on the image enhancement eect, the house image was still used as the experimental image. The threshold initial value was T0 0. Fig. 6 shows the result graphs o the house image enhancement under n, n 5, and n 8. (c) T0 30 (d) T0 50 Fig.7. Result Graphs o the House Image Enhancement under Dierent (a)original House Image (b) 4. Comparative stud with other algorithms The data used in the experiment were classical and lowcontrast images collected in an industrial site. To compare the algorithm in this chapter with similar algorithms, classical images and collected low-contrast images were selected in this section or algorithm test. In addition, a comparative stud o the proposed algorithm with the traditional Laplace algorithm would be implemented. The relevant parameters were regulated in the experiment to make the processing results o all algorithms reach avorable visual eects. The experimental results o the cameraman image are shown in Fig. 8. Accordingl, the traditional Laplace algorithm enhanced the details o the camera and hair in the image; however, evident burrs were generated in the overcoat sleeves and collar edges. However, the proposed algorithm enhanced the image details and eectivel n (c) n 5 (d) n 8 Fig. 6. Result Graphs o the House Image Enhancement under Dierent Numbers o n Iterations 69

7 Yan Chen, Yunjiao Bai, Quan Zhang, Yanling Wang and Zhiguo Gui/ Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 inhibited the burr generation with the avorable visual eect. The traditional Laplace algorithm ampliied the noise and enhanced the details with man nois points in the image. The image enhanced b the proposed algorithm was relativel clear, and the noise enhancement was inhibited, thereb obtaining a substantial enhancement eect. image, although it evidentl ampliied the noise o the nose and ee corner details. In the image processed b the proposed algorithm, the baboon hairs were clear, the noise in the ee corners was evidentl relieved, the noise was relativel smooth, and the visual eect was avorable. (a) Original Image (a) Original Image (b) Laplace Algorithm (b) Laplace Algorithm (c) Proposed Algorithm Fig.9. Comparison o the Enhancement Eects o the Baboon Image (c) Proposed Algorithm Fig.8. Comparison o the Enhancement Eects o the Cameraman Image Table lists the objective indexes o the images obtained ater processing using the two algorithms. The image processed b the proposed algorithm had high peak SNR with low MSE. These results were consistent with subjective observation results. The image enhanced b the proposed algorithm had extensive detailed inormation and its noise was inhibited. To speci the enhancement eect o the new algorithm in a considerabl objective manner, Table lists the peak signal-to-noise ratios (SNR), the mean squared errors (MSE), and the inormation entropies o all enhanced images in Fig. 8. Table shows that the image entrop values obtained through the proposed algorithm and the Laplace algorithm were identical. However, the image obtained through the proposed algorithm had high SNR and low MSE with the improved image qualit. These results were consistent with those obtained through subjective observation. Table. Objective Indexes o Algorithms or Baboon Laplace algorithm Proposed algorithm Table. Objective Indexes o the Algorithms or the Cameraman Image Laplace algorithm Proposed algorithm PSNR(db) MSE PSNR(db) MSE IE(bit) Fig. 0 shows the eect comparison o the low-contrast industrial image collected on the site and processed through the traditional Laplace enhancement and the proposed algorithms. The parameters include the initial value o the threshold value o T0 and the number o iterations was n 5. Fig. 0 shows (a) the industrial image, (b) the image IE(bit) Fig. 9 shows the eect comparison o a baboon image enhancement using the traditional Laplace and proposed algorithms. The ormer enhanced the hair details in the 70

8 Journal o Engineering Science and Technolog Review 0 (3) (07) 64-7 enhanced using the Laplace algorithm and (c), the image enhanced b the proposed algorithm. (a) Industrial Image Fig.. Histograms o the Objective Indexes o the Industrial Image 5. Conclusions (b) Laplace Algorithm (c) Proposed Algorithm Fig. 0. Comparison o the Enhancement Eects o the Industrial Image Table 3 provides the objective evaluation indexes o the lowcontrast industrial image ater processing using the dierent methods. To intuitivel compare the objective indexes o the dierent algorithms, Fig. shows the histograms o the objective indexes o the low-contrast image enhancement. The Laplace algorithm was sensitive to noise; hence, it had high inormation entrop. However, the image processed b the proposed algorithm had high peak SNR and low MSE, thereb suicientl certiing the advantages o the proposed algorithm. Table 3. Objective o the Indexes o the Algorithms or the Industrial Image PSNR(db) MSE IE(bit) Laplace algorithm Proposed algorithm To solve the problem in which the traditional Laplace algorithm ampliied noise and enhanced image details, the local variance was adopted to classi image eatures, the change characteristics o the trigonometric unction were used to construct the diusion coeicients, an anisotropic image enhancement method was proposed, and the inluences o the multiple parameters on image enhancement were analzed through simulation experiment. The conclusions obtained are as ollows. ()The image enhancement algorithm model established based on the local variance can accuratel calculate the local neighborhood eatures o the pixels and relect the change eatures o the image details during the pixel diusion process. ()The comparison o the enhancement eects between the proposed algorithm and traditional Laplace algorithm indicates that the anisotropic diusion method can dierentiate image noise and details. In addition, the organic combination o the sel-adaptive threshold value and the diusion can solve the problem o sensitivit o diusion to noise. (3) The simulation analsis o the low-contract image enhancement algorithm shows that low-contrast image detail inormation is substantiall protected b regulating the number o iterations o tangential direction parameters. In addition, the peak SNR reaches 30.85, MSE is as low as 7.333, inormation entrop remains unchanged, the expected eect is obtained, the threshold value parameter is reasonable, and the enhancement eect o the improved algorithm is stable. The neighborhood eatures in the proposed algorithm can and accuratel relect the local eatures o the pixels in consideration o the local variances o the pixel. This process aims to provide a substantiall accurate technical support or the image enhancement method based on partial dierential equation. However, the parameter setting o the enhancement model needs regulation b experience. Thus, a man machine interaction mode or parameter setting should be established. This is an Open Access article distributed under the terms o the Creative Commons Attribution Licence 7

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