DWT based Novel Image Denoising by Exploring Internal and External Correlation

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1 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 397: 007 Certfed Organzaton) DWT based Novel Image Denosng by Explorng Internal and External Correlaton Arun Kumar.R 1, Sathdasan.K, Balasubramanan. T 3 PG Scholar, Dept of ECE, Adhparasath Engg College, Melmaruvathur, Inda 1&3, Assstant Professor, Dept of ECE, Adhparasath Engg College, Melmaruvathur, Inda ABSTRACT: Removng nose from the orgnal mage s stll a challengng problem for researchers. In ths paper, we buld the nternal and external data cubes to fndng the smlar patches from the nosy and web mages respectvely. We proposed two-stages usng dfferent flterng approaches for reducng nose. By combnng the nternal and external de-nosng patches, we obtan a prelmnary de-nosng result. In the second stage, we propose reducng nose by flterng of external and nternal cubes, respectvely, on transform doman. In ths stage, the prelmnary de-nosng result not only enhances the patch matchng accuracy but also provdes relable estmates of flterng parameters. The fnal de-nosng mage s obtaned by fusng the external and nternal flterng results process. The proposed system shows the obsolete de-nosng results wth the accurate measurements, and ts compared wth exstng BM3D result, Low-ran based result, EPLL g result, EPLL s. KEWORDS: Image de-nosng, external correlatons, nternal correlatons, Dscrete wavelet transforms (DWT) I. INTRODUCTION Dgtal mage plays an mportant role n our daly lfe and n the area of research and technology. When the dgtal mage s transmtted from one place to another place, durng the transmsson nose s added nto the mage. Any form of sgnal processng havng mage as an nput and output s called mage processng. Image processng s a method to perform some operatons on an mage, n order to get an enhanced mage or to extract some useful nformaton from t. It s a type of sgnalprocessng n whch nput s an mage and output may be mage or characterstcs/features assocated wth that mage [1-3]. There are two types of methods used for mage processng namely, analogue and dgtal mage processng.the purpose of mage processng s dvded nto 5 groups. They are Vsualzaton, Image sharpenng and restoraton, Image retreval, Measurement of pattern, Image Recognton. Dgtal mages are often degraded by nose n the acquston and transmsson phase. The goal of mage de-nosng s to recover the true orgnal mage from such a dstorted nosy copy. The presence of nose not only produces undesrable vsual qualty but also lowers the vsblty of low contrast objects. The mage usually has nose whch s not easly elmnated n mage processng. Accordng to actual mage characterstc, nose statstcal property and frequency spectrum dstrbuton rule, people have developed many methods of elmnatng noses, whch approxmately are dvded nto space and transformaton felds. The space feld s data operaton carred on the orgnal mage, and processes the mage grey value, le neghborhood average method, wener flter, center value flter and so on. Varous types of nose present n mage are Gaussan nose, Salt & Pepper nose and Specle nose [1]. Image de-nosng technques are used to prevent these types of noses whle retanng the mportant sgnal features [1]. Poor mage sensors, mperfect nstruments, problems wth data acquston process, transmsson errors and nterferng natural phenomena are ts man sources [4-5]. Therefore, t s necessary to detect and remove noses present n the mages. Reservng the detals of an mage and removng the random nose as far as possble s the goal of mage de-nosng approaches. Copyrght to IJIRSET DOI: /IJIRSET

2 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 397: 007 Certfed Organzaton) In ths paper Secton II descrbes the related wor. Secton III llustrate the results and dscussons. Secton IV concludes the paper. II. RELATED WORK De-An Huanget al [6], have proposed novel self-learnng based mage decomposton framewor. Based on the recent success of sparse representaton, the proposed framewor frst learns an over-complete dctonary from the hgh spatal frequency parts of the nput mage for reconstructon purposes. Madson Gray McGaffn et al [7] have proposed mage de-nosng algorthms for edge preservng regularzaton that play to the strengths of GPUs, the exemplar of ths parallelsm trend. By avodng operatons le nner products or complex precondtoners and mnmzng memory usage, the proposed GCD algorthms provde mpressve convergence rates. The addtonal ncrease n performance provded by Nesterov s frst-order acceleraton s exctng. Xanhua Zeng et al [10], have proposed a two-dmensonal mage de-nosng model, namely, the Dctonary Par Learnng (DPL) model, and we desgn a correspondng algorthm called the Dctonary Par Learnng on the Grassmann-manfold (DPLG) algorthm. Gven a nose free mage Fg. 1. Proposed Method 0 P (the superscrpt o denotes orgnal), ts nosy verson P s produced as P P 0 N (1) Where N s the addtve zero-mean ndependent dentcally dstrbuted (.e.) Gaussan nose wth varance. We am to recover, the estmated nose free verson of P, wth the help of web mages. For one nosy nput P, we retreve ts correlated mages P, P,...,P } (the superscrpt r denotes reference { 1 mages) from web mages usng content based mage retreval technology. Gven I and P, P,...,P }, we propose a two-stage based de-nosng scheme, whch s llustrated n Fg. 1. { 1 Copyrght to IJIRSET DOI: /IJIRSET

3 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology A. Correlated Image Retreval and Regstraton (An ISO 397: 007 Certfed Organzaton) The local features such as SIFT are usually of dfferent scales and overlapped wth each other. We beleve the ncluson relatonshp among local features provdes a natural way to construct vsual phrases for geometrc verfcaton.prevous learnng based de-nosng methods gnore content prors n a nosy mage, whch lmts mprovement n de-nosng performance. Therefore, we adopt content-based mage retreval technology, specfcally the scale nvarant feature transform (SIFT) based method proposed n to retreve correlated mages from a large-scale database, as our external dataset. Snce a large scale SIFT feature may cover multple small scale SIFT features. After matchng all the vsual groups extracted from the nosy mage wth those extracted from canddate mages, we obtan a set of correlated mages r r { P1, P,...,P r }.For more detals; please refer to [6]. Note that, to reduce the mpact of nose n feature extracton, we dscard some ey-ponts wth low contrast. B. Graph Based Optmzaton Method-External De-Nosng usng Baselne Method: dstances as the matchng crteron. Let x, y, z An ntutve method s to use be the translaton vector (n col, row, and mage number drectons) of the t patch from the nosy mage to the reference mages. We want to fnd the optmal translaton for each patch by mnmzng the followng functon: t E(t) D ( X, ( t )), () Where D ( X, ( t )) s dstance of patch P and the canddate patch Q wth translaton t. Note that the mean values of all the canddate patches and the nosy query have been removed to reduce llumnaton effect. The translaton vectors are obtaned by mnmzng the followng energy functon: E ( t) D ( X, ( t )) (, j ) N S ( t, t j ) (3) Where X, ( t )) D s the same as that defned n Eq. 3 t, t ) ( S s the smoothness term, β s a weghtng parameter th th to adjust the effect of the smooth constran and (, j) N denotes that the I and j patch are neghborng patches. In ths paper, N s a four connected grd. The smooth term S penalzes the dfferences of neghborng translaton vectors, ( j mn( t t j 1, 1 ), fz z j 0 defned as S ( t, t j ), C. Second Stage Reducng Nose by Flterng Process 1st The frst stage de-nosng result I has greatly reduced the nose. Therefore t could help to mprove the de-nosng performance n the second stage. A smlar strategy s proposed n BM3D [] and have acheved sgnfcant gan compared wth the frst stage de-nosng result. In ths secton, we wll frst ntroduce Wener flterng n mage denosng, and then apply t to our nternal and external de-nosng scheme. D. De-Nosng by Dwt Process The wavelet transform s a type of sgnal transform that s commonly used n compresson or for decomposton of the error sgnal. A newer alternatve to wavelet transform s the dscrete wavelet transforms. Mult wavelets are very smlar to wavelets but have some mportant dfferences. In partcular, Mult wavelets have two or more scalng and (4) Copyrght to IJIRSET DOI: /IJIRSET

4 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 397: 007 Certfed Organzaton) wavelet functons whereas wavelets have an assocated scalng functon (t) and wavelet functon (t) 1 r T ( t ) [ ( t), ( t),... ( t)], where ). The set of scalng functons can be wrtten as (t s called the mult scalng functon. Lewse, the Mult wavelet functon s defned from the set of wavelet functons as T ( t ) [ 1( t) ( t)... r ( t)]. Where,T s denoted as the vector response and r can be arbtrarly large (where r=) [31]. The mult wavelet relaton of low pass flter and hgh pass flter s as follows. ( t) n 1 R ( 0 t ) (5) ( t) n 1 L ( 0 t ) (6) Where, H s the low pass flter coeffcent and G s the hgh pass flter coeffcent. The speed error of the IPMSM drve system s appled as an nput to the mult wavelet networ, so that the error gets decomposed. In mult wavelet transform, the decomposton produces --two low-pass sub-bands, two hgh-pass sub-bands and two sets of wavelet coeffcents for the each level of decomposton. Ths transformaton detects and extracts dsturbance features n the form of low and hgh frequency nformaton. Mult scalng functons and Mult wavelet functons avalable n mult wavelets led to better decomposton of error n each level of decomposton. E. De-Nosng by Explorng External Correlatons ~ ~ ~ The nose n I 1st s assumed to be greatly attenuated. Therefore, for each patch P 1st n I 1st, we can drectly retreve ts -NN patches { Q1, Q,...,Q } from the external mage set { P1, P,...,P } usng the baselne method descrbed n Sec. V-A. These patches q, q,..., q } (n vector format) can be regarded as sample realzatons of { 1 0 P Note that DC components of { q, q,...,q } 1 and p are removed before patch matchng to reduce llumnaton effects. Therefore, the matrx R s estmated as: R R D T T D III. RESULT AND DISCUSSION Our proposed DWT method performance s analyzed by comparng the exstng technque such as BM3D, LOW RANK, EPLLg and EPLLs wth our proposed technque. The sample mage whch s utlzed n the mage de-nosng s gven below n fgure to fgure 5.the PSNR value of the proposed DWT based de-nosng technque s compared wth the exstng BM3D, Low-ran, EPLLg, EPLLs. Copyrght to IJIRSET DOI: /IJIRSET

5 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 397: 007 Certfed Organzaton) Fg..Shows the sample mages taen from the database Fg. 3. Shows the grey scale mages Fg.4.Shows the nose mages Fg. 5. Shows the de-nosed mage. TABLE I PSNR & SSIM COMPARISION Image BM3D BM3D Proposed Proposed PSNR SSIM PSNR SSIM a b c From the table t has been shown that the PSNR value of the proposed technque s hgher than the exstng technques. Copyrght to IJIRSET DOI: /IJIRSET

6 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 397: 007 Certfed Organzaton) Smlarly the SSIM values of the proposed dwt and exstng BM3D method was compared. The SSIM value of the proposed technques gves hgher performance rato than the exstng technque IV. CONCLUSION We have proposed two stages of dfferent flterng approaches. In the frst stage external de-nosng part, a graph-cut based patch matchng accuracy. The nternal de-nosng part s performed on smlar nosy patches by flterng n the transform doman. In the second stage reducng nose by flterng process. Then, to mprove the second stage denosng result; we have proposed DWT for flterng on nosy cubes n nternal de-nosng. By combnng the nternal and external de-nosng patches, we obtan the fnal de-nosng result. The expermental results demonstrate that our proposed de-nosng method acheve compettve performance than other exstng methods. REFERENCES [1] Danel J. Strauss, Tanja Teuber, Gabrele Stedl, Farah I. Corona Strauss, Explotng the Self Smlarty n ERP Images by Nonlocal Means for Sngle Tral Denosng, IEEE Transactons, 015. [] Camlle Sutour, Charles-Alban Deledalle, and Jean-Franços Aujol, Adaptve Regularzaton of the NL-Means: Applcaton to Image and Vdeo Denosng, IEEE transactons on mage processng, vol. 3, no. 8,014. [3] Lnln Xu, Jonathan L, SAR Image Denosng va Clusterng-Based Prncpal Component Analyss, IEEE transactons on geoscence and remote sensng, vol. 5, no. 11, november 011. Copyrght to IJIRSET DOI: /IJIRSET

7 ISSN(Onlne): ISSN (Prnt): Internatonal Journal of Innovatve Research n Scence, Engneerng and Technology (An ISO 397: 007 Certfed Organzaton) [4] Lnln Xu, Fan L, Alexander Wong, Davd A. Claus, Hyperspectral Image Denosng Usng a Spatal Spectral Monte Carlo Samplng Approach, IEEE journal of selected topcs n appled earth observatons and remote sensng, vol. 8, no. 6, june 015. [5] Xuande Zhang, Xangchu Feng, and Wewe Wang, " Two-Drecton Nonlocal Model for Image Denosng, IEEE transactons on mage processng, vol., no. 1, 013. [6] De-An Huang, L-We Kang, u-chang Fran Wang, Adaptve Regularzaton of the NL-Means: Applcaton to Image and Vdeo Denosng, IEEE transactons on mage processng, vol. 3, no. 8,014. [7] Madson Gray McGaffn, d Jeffrey A. Fessler, Edge-Preservng Image Denosng va Group Coordnate Descent on the GPU, IEEE transactons on mage processng, vol. 4, no. 4, november 015. [8] Lng Shao, Ruome an, Xuelong L, Fellow, an Lu From Heurstc Optmzaton to Dctonary Learnng: A Revew and Comprehensve Comparson of Image Denosng Algorthms, IEEE transactons on cybernetcs, vol. 44, no. 7, 014. [9] Wangmeng Zuo, Le Zhang, and Wewe Wang, Chunwe Song, Davd Zhang " Gradent Hstogram Estmaton and Preservaton for Texture Enhanced Image Denosng, IEEE transactons on mage processng, vol. 3, no. 6, 014 [10] Xanhua Zeng, We Ban, We Lu, Jale Shen, Dacheng Tao, Fellow, Dctonary Par Learnng on Grassmann Manfolds for Image Denosng, IEEE transactons on mage processng, 015. Copyrght to IJIRSET DOI: /IJIRSET