An Automatic Image Enhancement Technique for Low Contrast. Image 1. INTRODUCTION

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1 An Automatc Image Enhancement Technque for Low Contrast Image Chen-Chh Wang Department of Industral Engneerng and Management Mng Ch Unversty of Technology, Tape 43, TAIWAN Emal: Bernard C Jang 1 Yueh-Sha Chou Chen-Cheng Chu 3 Department of Industral Engneerng and Management Yuan-Ze Unversty, Chung-L 3003, TAIWAN Emal: ebjang@saturnyzuedutw 1 chou_sha@msncom s99507@malyzuedutw 3 Abstract In applcaton of machne vson nspecton, mage enhancement s an essental procedure In practcal applcaton, mage enhanced method s usually be appled on mage pre-processng to make nspecton more effectve The mages enhanced method usually select through tryng errors or experence rules The research proposed an automatc mage enhanced method to make the problem be solved speedly and effectvely The research use multvarate analyss to set up automatc mage enhanced select mode At frst, chosen optmal enhanced mage that already n the lterature to buld the bass of the model Then, calculatng the egenvalue and setup dscrmnate functon After settng up dscrmnate functon, t can make use of Wlk's statstc to choose the characterstc In procedures of automatc mage enhancement has two man decson stages: 1) Make sure to use the spatal doman or frequency doman ) The second stage s to determne ts correspondng enhanced method accordng to the result at frst stage Usng 53 tranng mages to set the model and has 9811% accurate rate to select the most sutable method The select methods fully conform to the samples chrematstcs The results of 44 mages through par t-test to verfy can fnd that p- value s 0 and average ncreasng contrast value s 551 The proposed procedure s sure effectvely Keywords: Machne Vson Image Contrast Image Feature Dscrmnate Analyss 1 INTRODUCTION Image enhancement s a very mportant step n the detecton of mages n machne vson applcatons In practcal applcatons, f good mages can be acqured, wth no dfferences n lghtng, rotaton, shftng, and so on, then t s smple to detect the poston of a defect usng mage subtracton between the two mages However, t s very dffcult to acheve good mages n a natural envronment and durng contnuous operatons For example, when equpment s n operaton, shock nevtably occurs, causng dsplacements At the same tme, varatons n the operatng envronment s llumnaton can cause lghtng changes n mages On the other hand, t s usual to capture mages wth low-contrast or napproprate placement These are dffcult to smooth and unrealstc, decreasng rejectons n actual practce These also cause operator or machne judgment errors When an mage has a hgher contrast value and a clear edge, operators can more easly determne the qualty of ther products A hgher contrast rato dfference between the subject and the background also makes t easer to cut apart a smple mage (Jang et al, 004) To make practcal machne vson nspecton more effcent, numerous studes have been done on methods to enhance an mage before measurng and classfyng the defects The man purpose of mage enhancement s ncreasng the contrast of an mage and decreasng characterstc that does not need t The lterature shows that mage enhancement technology usually nvolves tral and error or experence rule methods Ths research proposes an automatc mage enhancement algorthm The proposed method uses the egenvalues of mage characterstcs to construct a procedure wth optmum results Ths method was able to quckly provde captured 340

2 mages wth a hgher contrast It could also smplfy and speed up subsequent treatments, such as the detecton of defects and problem categorzaton The mage enhancement technology s manly dvded nto spatal and frequency domans Tsa and Huang (003) presented a global approach for the automatc nspecton of defects n randomly textured surfaces, such as sandpaper, castngs, leather, and many ndustral materals The proposed method does not rely on local texture features It s based on a global mage reconstructon scheme usng the Fourer transform Ths converts the dffcult problem of defect detecton n textured mages nto a smple threshold problem for non-textured mages Ln and Ho (004), focusng on the defects n needles, proposed usng twodmensonal DCT to change mages from the spatal doman to the frequency doman Ther method could enhance an mage and make t easer to fnd the hole n a needle From the above dscusson, t s obvous that mage enhancement s an essental part of mage processng If fast mage enhancement technques can be devsed and put nto practcal applcaton, these can shorten the tme needed for detecton and fll the demand of manufacturers for faster nspecton Ths research prmarly devsed a set of procedures for automatc mage enhancement Ths enhancement technology mproves the contrast value of mages, makng them easer to recognze The method uses multvarate analyss to set up an automatc mage enhancement selecton model Frst, the optmal mage enhancement methods dscussed n the lterature were chosen as the bass for the model, then egenvalues were calculated and a dscrmnate functon was set up After settng up the dscrmnate functon, Wlk s statstc could be used to choose the characterstc The automatc mage enhancement procedure has two man decson stages: 1) Choose between the spatal doman and frequency doman ) Determne the correspondng enhancement method for that doman METHODOLOGY Frst, calculate the egenvalues of the fetched mage and then set up a dscrmnate functon n the frst stage Ths stage should manly dstngush between the frequency doman and spatal doman The logc and methods of dealng wth the mage dffer between the spatal doman and frequency doman So, n order to get better enhancement results, t s necessary to choose the most sutable analyss doman Then, the dscrmnate functon s set up on ths doman and the optmal method of enhancement s chosen n the second stage 1 Image Feature Defnton Frst, a quantzaton procedure for the characterstcs s appled to the fetched mage For ths mage characterstc quantzaton, egenvalues are calculated based on the gray l evel values and used for a Fourer transform Two methods can be used to determne gray level value based characterst cs to descrbe an mage The frst uses statstcs ( C1, C, C3 ) and the other one s an mage estmatng method ( C4, C5, C 6 ) The defntons are as follows: Average: To represent the average brghtness of an mage as C 1 Standard devaton: To represent the degree of the mage s complexty as C Entropy: To represent the degree of the mage s complexty and uncertanty as C 3 The range of gray level values spread throughou t the mage: A hgher value represents more gray level values, whch are shown as C 4 The varaton n gray level values for every regon n an mage: Ths ndcator s represented by C 5 Contrast value of mage: Ths value s calculated through a gray level hstogram The value descrbes the gray level value dfferences n an mage, wth bgger values representng sharper mages Ths value s represented by C 6 For a defnton of an egenvalue and Fourer transform based method, we can refer to the ndcators from Tsa and Tseng (1999) There are fve ndcators that can be used to descrbe an mage s characterstcs after Fourer transform They are the proporton of energy n the R 1 regon, the proporton of energy n the R regon, the proporton of energy n the R 3 regon, the whole power spectrum of the mage s relatve entropy, and the sum of the hghest 1% of the power spectrum energy The central part of the spectrum s (u, v)=(0, 0), and the sze of the spectrum s M N The shape of the mage s a square so that M = N Set the dstance from the central part of the spectrum to the edge of the spectrum as M and N, such that M = 3 / 4M and N = 3 / 4N M and N are dvded nto three sub-regons, so that the spectrum can be separated nto three regons The defntons of the egenvalues for the Fourer trans form are as follows: The proporton of energy n the R 1 regon sho ws as C 7 The proporton of energy n the R regon sho ws as C 8 The proporton of energy n the R 3 regon sho ws as C 9 The whole power spectrum of the mage s relatve entropy shown as C 10 The sum of the hghest 1% of the power spectrum energy: shown as C

3 Image Enhanced Method Model Determnng whch varables should be used for an m age enhancement method can be dffcult Ths research use d the dscrmnate functon proposed by Fsher (1936) to set up a model Snce the tranng mages for the model were al most all from past researches, the problem nvolved superv sed classfcaton The dscrmnate functon was used for th e model because t has the characterstcs of requrng fewer hypotheses and beng smple From Secton 1, the chara cterstc varables are C 1, C, C 3, C 4, C 5, C 6, C 7, C 8, C 9, C 10, C 11, and set s dscrmnate functon d s d ( ) S C = C C C S C (1) where C = [ C1, C,, C11 ] are the feature varables for an ' unknown class, C = C1, C,, C 11 s the sample mean of the feature varables for the - th class, and S s the sample varancecovarance matrx of the feature varables for -th class Allocate C to the k class f the dscrmnate functon dk ( C ) =the largest of d ( C ), =1,,,g Before begnnng the dscrmnate functon analyss, the feature varables sho uld frst be evaluated to construct a hgh performance mode l Ths research used Wlk s lambda of MANOVA to do the evaluaton The Wlk s lambda s a test statstc used n the multvarate analyss of varance (MANOVA) to test wheth er there are dfferences between the means of dentfed gro ups of subjects on a combnaton of dependent varables 1 Selecton of S patal D oman s I mage E nhancement Method Ths research used sx knds of mage enhancement m ethods from the lterature (Gonzalez and Woods, 199) Th ese methods helped establsh the spatal doman model for mage enhancement They ncluded hstogram equalzaton, unform flter, medan flter, Laplacan flter, sharpness, and gradent The spatal doman features (C 1, C, C 3, C 4, C 5, C 6) and frequency doman features (C 7, C 8, C 9, C 10, C 1 1) were used as ndependent varables The sx selected spat al doman mage enhancement methods were used as the d ependent varables Then, the dscrmnate functon could b e set up The model was set up as follows: Step 1: Select n mages from the lterature and apply t he sx mage enhancement methods to the mages Th ese mages wll be used as tranng mages for the mo del Step : Calculate the egenvalues for the n tranng m ages, namely C 1, C, C 3, C 4, C 5, C 6, C 7, C 8, C 9, C 10, C 11 Step 3: Use the data from steps 1 and to set the z Step 4: If Spataldoman d = β + β C + + β C = 1,,3,4,5,6 = 1,,3,4,5,6 j () Max d = d, the mage s most su table spatal doman enhancement s the j algorthm Selecton of the s I mage Enhancement Method Ths research used a low-pass flter and a hghpass flter n the automatc mage enhancement model for t he frequency doman Although there s a lot of hgh freque ncy data at the edge of an mage and the gray level values c hange after Fourer transform, the use of a flter n the frequ ency doman can smooth mages The basc model for the fr equency doman flter s as follows: G( u, v) = F( u, v) H ( u, v) (3) The man purpose was to select a flter transform functon H ( u, v ), and reduce F( u, v ) s hgh frequency weght to ge t G( u, v ) The transform functon for an deal twodmensonal low pass flter s as follows: ( ) ( ) 1 f D u, v D0 H ( u, v) = 0 f D u, v > D0 where D 0 s a non-negatve constant and (, ) dstance from ( u, v ) to the orgn as follows: M N D( u, v) = u + v 1/ (4) D u v s the All of the weghts can pass wthout reducng the crcle wth radus D 0, but outsde the crcle wll be reduced On the other hand, a hgh-pass flter can be used to sharpen the mage A hgh-pass flter s the opposte of a low-pass flter, wth ts functon as follows: Hhp ( u, v) = 1 Hlp ( u, v) (6) where Hlp ( u, v ) s the transform functon for the deal lo w-pass flter It must be shown as follows: ( ) ( ) 0 f D u, v D0 H ( u, v) = 1 f D u, v > D0 where D 0 s a non-negatve constant and D( u, v ) s the dstance from ( u, v ) to the orgn The flter s the opposte of the low-pass flter n that all of the weghts wll be set to 0 n the crcle wth radus D 0, and the outsde of the crcle wll be passed (5) (7) Settng up the model for the automatc mage enhance 34

4 ment method on the frequency doman s as same as for the spatal doman The model uses spatal doman egenvalues ( C1, C, C3, C4, C5, C 6 ) and frequency doman egenvalues ( C7, C8, C9, C10, C 11 ) as ndependent varables, and the two s elected frequency doman mage enhancement methods as dependent varables The dscrmnate functon can be set a s n followng steps: Step 1: Select n mages from the lterature that are ap proprate for enhancement by the two methods to use as tranng mages Step : Calculate the n mages egenvalues C 1, C, C 3, C 4, C 5, C 6, C 7, C 8, C 9, C 10, C 11 Step 3: Based on the data from steps 1 and, set up t he dscrmnate functon doman d = β + β C + + β C = 1, Step 4: If Max d = 1, j (8) = d, the mage s most sutable en hancement method for the frequency doman s the j method 3 Evaluaton Index Ths study used contrast values to quantfy mages Th e defnton of contrast value s as follows: where Contrast value = Max f = ( f µ ) 0 f n M N f (9) µ f = the average gray level value for an mage, n f = the total number of gray level values equal to f, Max = the maxmum gray level value n the mage, M N = the sze of the mage To compare the results after mage enhancement, ths research used a pared t-test for evaluaton Frst, n orgnal mages were selected and the contrasts { t1, t,, t n } were calculated Then the proposed automatc mage enhancement model was appled to the n mages Fnally, after enhancng, the contrasts were calculated as { s1, s,, sn} The procedure for ths pared t-test s as follows: Step 1: Calculate d = s t, = 1,,3,, n Step : Calculate { d d d } standard devaton 1,,, n s average d and s d d Step 3: If p value = p tn 1 > > 005, t sd n n 1 s the t dstrbuton of DOF equal to n 1, and the results are outstandng 3 EXPERIMENT ANALYSIS and RESULTS Frst, 53 mages were used to set up the models for the spatal doman and frequency doman The spatal doman egenvalues (C 1, C, C 3, C 4, C 5, C 6 ) and the Fourer transform values (C 7, C 8, C 9, C 10, C 11 ) were used as ndependent varables to set up the dscrmnate functon To obtan an optmal dscrmnate functon, the Wlk s test was used frst The egenvalues where the p-value was less than 005 were C 1, C 3, C 4, C 5, C 7, C 8, and C 10 As a result C 1, C 3, C 4, C 5, C 7, C 8, and C 10 were used for the dscrmnate functon The results are as follows: spataldoman d = C + 190C C 39931C 011C + 304C (10) 7677C frequencydoman d = C C C 345C + 081C + 197C (11) 8445C Wth the mage s egenvalues appled to the dscrmnate functon, f equal to d frequencydoman spacedoman d was bgger than or, then the mage was sutable for enhancement on the spatal doman, otherwse the frequency doman was used Usng the 53 tranng mages for evaluaton, the method was found to be 100% effectve PCB, IC, and BGA mages were sutable for spatal doman enhancement Ths was not only because of the mages ch aracterstcs but also because they ncluded geometrc featu res, lke crcles, lnes, and squares So, PCB type mages or those wth geometrc features are sutable to be enhanced o n the spatal doman LCD, ITO, and surface barrer layer 343

5 mages were sutable for frequency doman enhancement T hs was not only because of the mages characterstcs but also because they nclude low contrast or random textures So, semconductor type mages or those wth laws or rando m textures are sutable for frequency doman enhancement Table 1 shows the results of the 13 test mages u sed to evaluate the model Test Images Table 1: Test mage results C 1 C 3 C 4 C 5 C 7 C 8 C spataldoman frequencydoman 10 d d Results

6 It was suggested that the frst seven mages be enhanc ed on the spatal doman and the other sx on the freq uency doman Ths proves that the proposed model s effectve 31 Evaluaton and Results of Selectng a Method on the Spatal Ths secton dscusses the selecton of an mage enhancement method for the spatal doman The 38 tranng mages from 31 were used, and the responded values were assumed to be sutable methods (hstogram equalzaton, unform flter, medan flter, Laplacan, sharpness, and gradent) The egenvalues defned n 1 (C 1, C, C 3, C 4, C 5, C 6, C 7, C 8, C 9, C 10, C 11 ) were then used as ndependent varables to set up the dscrmnate functon Frst, based on the results of Wlk s test, the egenvalu es C 1, C 3, C 4, C 5, C 6, C 7, C 8, and C 9 were used to set up a d scrmnate functon, attanng a hghest accuracy rate of 97 37%The research selected the dscrmnate functon as the model for determnng the enhancement method n the spat al doman Images wth bgger features or a smple foregrou nd wll use the average flter Equalzaton wll be used on mages wth a complex background or texture On the other hand, the Laplacan flter s used on mages wth smaller fe atures or an unclear background and the medan flter on m ages wth a smple foreground and clear background Fnall y, the sharpen flter s used for mages wth a unform back ground and the Sobel s usually appled to IC or PCB mage s Seven test mages were selected to evaluate the method From Table 4, test mages 1 and have bgger features and smple foregrounds Test mages 3 to 5 have complex crcuts or complex backgrounds Test mage 6 has a complex foreground and test mage 7 has a clear background The proposed automatc spatal doman mage enhancement model can select the most sutable algorthm 3 Evaluaton and Results of Selectng Method for The secton dscusses the selecton of the mage enhancement methods for use wth the frequency doman Ffteen tranng mages from 31 were used, and the sutable methods were assumed to be responded values (hgh-pass flter and low-pass flter) The egenvalues defned n 1 (C 1, C, C 3, C 4, C 5, C 6, C 7, C 8, C 9, C 10, C 11 ) were used as ndependent varables to set up the dscrmnate functon Frst, based on the results of the Wlk s test, the most senstve egenvalues, C 1, C 7, C 8, were used to set up the model Ths model had an accuracy rate of 100% We found that the hgh-pass flter s approprate for use wth mages that have a unform foreground On the other hand, the low-pass flter s best used for mages wth bgger defects and a randomly structured texture Fve test mages were used to evaluate the model The decson was made to use the hgh-pass flter for test mages 8 and 9, whch are unform mages Test mages 10 to 13 also had the same results To summarze, the proposed algorthm can select the most sutable mage enhancement method for use wth the frequency doman 33 Contrast Value Evaluaton The secton dscusses how contrast value was used to evaluate whether there was a clarty mprovement n the mages after applyng the proposed method The evaluaton method was pared wth a t-test and f the p-value of the result was greater than or equal to 005, the proposed method was consdered to be effectve Frst 44 orgnal mages were selected and contrast values were calculated Then the proposed model was used to select the mage enhancement algorthm Ths algorthm was appled and the contrast value was calculated agan The proposed method produced mages that were actually better than the orgnals Then, through a pared t-test, we found that the 95% CI for the mean dfference was (-301, -1901) for α =005 and a P-Value = 0000 Ths proves that the proposed method s effectve After the mage enhancement method was selected usng the 44 mages, the average ncreasng contrast value was 551 To summarze, we used Otsu s method, a popular thres holdng method to verfy the results Table 3 shows the results of usng Otsu s method on the orgnal mage and the mage enhanced wth the proposed method T hrough the proposed method, the man outlne could be pre served n mage 1, the nose was decreased n mage, and the defects were made clear n mage 3 and mage 4 The pr oposed automatc mage enhancement selecton model s ef fectve 4 CONCLUSION and DISCUSSION In practcal applcatons, mage enhancement s an ess ental procedure for machne vson nspecton The enhanc ed mages can help n analyses In real applcatons, an ma ge enhancement method s usually selected through tral an d error or by usng experence rules Past studes always us ed desgn rules Ths research used the knowhow from prevous studes to analyze egenvalues for mag es and sutable enhancement methods An automatc mage enhancement method was then constructed to quckly and e ffectvely solve the problem 345

7 Table : Results of classfcaton usng test mages Test mage d average d equal d laplacan d medan d sharpen d sobel Types Results sharpen sharpen equalzaton equalzaton equalzaton sobel medan 346

8 Table 3: The results of usng Otsu s on the orgnal mage and the mage enhanced wth the proposed method Orgnal Image Enhanced Image Orgnal Image By Otsu s Enhanced Image By Otsu s Ths method uses multvarate analyss to set up an automatc mage enhancement selecton model Frst, optmal mage enhancement methods were chosen from the lterature to buld the bass of the model Then, the egenvalues were calculated and the dscrmnate functon was set up After settng up the dscrmnate functon, Wlk s statstc was used to choose the characterstc The automatc mage enhancement procedure has two man decson stages: 1) Select ether the spatal doman or frequency doman ) Determne the correspondng enhancement method based on the result of the frst stage Ffty-three tranng mages were used to set up the model, achevng a 9811% accuracy rate n the selecton of the most sutable method The selected methods fully conformed to the samples features The results of verfcaton usng 44 mages wth a pared t-test found a p- value of 0 and an average ncreasng contrast value of 551 The proposed procedure s clearly effectve To sum up, the method proposed n ths research effectvely ncreases the contrast value of an mage The research used a statstcal method, rather than the past methods of tral and error or experence, to desgn rules The most mportant advantages of ths method are that an mage enhancement system can have a modular desgn and can ncrease the contrast values of mages quckly and wthout errors Ths method s very helpful on low contrast value mages that result from the envronment s nfluence ACKNOWLEDGEMENT Research was supported by the Natonal Scence Councl of Tawan, Project Number NSC 95-1-E MY3 REFERENCES Brand, S, Raum, K, Czurats, P, Hoffrogge, P (007) Sgnal analyss n scannng acoustc mcroscopy for nondestructve assessment of connectve defects n flp-chp BGA devces, IEEE Ultrasoncs Symposum,

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