Impulse Noise Removal Technique Based on Fuzzy Logic

Transcription

1 Impulse Nose Removal Technque Based on Fuzzy Logc 1 Mthlesh Atulkar, 2 A.S. Zadgaonkar and 3 Sanjay Kumar C V Raman Unversty, Kota, Blaspur, Inda 1 m.atulkar@gmal.com, 2 arunzad28@hotmal.com, 3 sanrapur@redffmal.com ABSTRACT Ths paper presents an algorthm to remove random mpulse nose n a dgtal grayscale mage based on fuzzy logc approach. Numbers of flters are used to remove nose usng fuzzy rules. Flter wndow used the local statstc to determne local weghted mean. Pxel that are detected nosy are fltered, other pxels are reman unchanged. Expermental results show that ths method has better performance than other state of the art flters. The effectveness of ths method s n terms of peak-sgnal-to-noserato. Key words: fuzzy logc, mage denosng, mpulse nose, flter. 1 Introducton In today s nformaton world mage act as an nformaton carrer lke navgaton system traffc observaton. Images are captured by use of satellte system vdeo camera, moble and other acquston devces. It may lkely possble mages are corrupted, blurred or degraded due to bad acquston of mages, transmsson of mages n electromagnetc felds or bad recordng[1]. Image conssts nformaton and snce mage s corrupted hence some useful nformaton of n mages are removed or mage s less nformatve. Dfferent types of nose are dentfed; some of them are mpulse nose, addtve (Gaussan) nose and multplcatve nose. In mpulse nose pxel value s replaced by a fxed value ether maxmum value or mnmum value sometmes t may be a random value usng random dstrbuton [2]. In addtve nose a fxed value s added to each pxel to the mage, added value s generated usng Gaussan dstrbuton hence t s commonly referred as Gaussan nose. In lterature many of them deal wth Gaussan nose. In ths paper we have to concentrate on mpulse nose. Durng lterature many flter found that deal wth stll mage corrupted wth nose [3-5]. Several works have been done on fuzzy logc whch has better performance compared to non-fuzzy approaches [6-8]. Many flter found that descrbe vdeo mage corrupted by Gaussan (addtve) nose [9-12]. Analogously t s necessary to made dstncton between flters for color mage and gray scale mage, flters for gray scale can be used n RGB color mage by applyng flter on each of the color band separately. Here we present a method for nose removal on a gray scale mage. The paper s structured as follows: n Secton 2 descrbe the methodology and dfferent steps n the actual flterng process are detaled n proposed method.secton 3 gves the expermental results. Fnally, n Secton 4 paper s concluded. DOI: /tmla Publcaton Date: 29 th December, 2014 URL:

2 Mthlesh Atulkar,A.S. Zadgaonkar and Sanjay Kumar; Impulse Nose Removal Technque Based on Fuzzy Logc, Transactons on Machne Learnng and Artfcal Intellgence, Volume 2 No 6 Dec (2014); pp: Proposed Denosng Algorthm Consder s a grayscale mage to be processed; w s a flterng wndow of pxel. Flterng wndow as n fgure 2.1 below centered at s surrounded wth no of neghbor ponts of dfferent layer. Ponts at vertcal and horzontal drecton of outer layer 1 s stored n and ponts at dagonals are stored n, smlarly ponts of layer 2 are stored n & and ponts of layer 3 are stored n &. In each no of nosy ponts are fnd out and stored n an array named. Fgure 1: Flter wndow of sze 7*7. vec N, S, W, ] (1) 1 [ E1 vec2 [ NW1, NE1, SW1, SE1 ] (2) vec N, S, W, ] (3) 3 [ E2 vec4 [ NW2, NE2, SW2, SE2] (4) vec N, S, W, ] (5) 5 [ E3 vec6 [ NW3, NE3, SW3, SE3] (6) vec [ val1, val2, val3, val4, val5, val6 ] (7) Where, val s no of nosy ponts n any vector ( vec ) s calculated by usng count () method as descrbed below for =1 to 6. val count vec ) (8) vec s provded as nput to the fuzzy nference system to evaluate fuzzy rules. If F( x, y) s corrupted and ponts of vec 1 s noseless than mean value of vec 1 s used to assst the restoraton of nosy pxel, but fvec1 s corrupted than vec2 s used to assst new value to nosy pont. It s not necessary for any vec that all ponts are noseless or corrupted. To solve ths problem fuzzy logc s used. Fuzzy rules are used to fnd thevec, whch are responsble to restore the nosy pxel. Flter wndow s 7 7 here but for pxel at corner a lmted flter of 5 5and 3 3sze s used. ( URL: 100

3 T r a n s a c t o n s o n M a c h n e L e a r n n g a n d A r t f c a l I n t e l l g e n c e V o l u m e 2, I s s u e 6, D e c Fgure 2: Expermental result wth Lena mage (a) standard Lena mage, (b) nosy mage wth 30% mpulse nose, Restoraton result by correspondng (c) FIRE[16], (d) BDND[17], (e) DWM, (f) LUO, (g) FMEM, (h) SAM, () PROPOSED METHOD flters. Fgure 3: Expermental result wth Gold Hll mage (a) standard Gold Hll mage, (b) nosy mage wth 30% mpulse nose, Restoraton result by correspondng (c) FIRE[16], (d)bdnd[17], (e) DWM, (f) LUO, (g) FMEM, (h)sam, () PROPOSED METHOD flters. C o p y r g h t S o c e t y f o r S c e n c e a n d E d u c a t o n U n t e d K n g d o m 101

4 Mthlesh Atulkar,A.S. Zadgaonkar and Sanjay Kumar; Impulse Nose Removal Technque Based on Fuzzy Logc, Transactons on Machne Learnng and Artfcal Intellgence, Volume 2 No 6 Dec (2014); pp: Fgure 2 presents vsual qualty of resultant denosed mage of varous methods. Fgure 2 (a) shows the orgnal lena mage. Fgure 2 (b) shows 30% nosy lena mage. Fgure 2 () shows the resultant mage of proposed method. Remanng fgures are output mage of descrbed methods. Fgure 3 shows the correspondng orgnal nosy and output mage of standard gold hll mage. 3 Result & Dscusson Experment s performed on two standard mage of Lena and gold hll mage. Value of peak-sgnal-tonose-rato (PSNR) and mean square error (MSE) n mages s calculated on the bass of below equaton (1) and equaton (2) PSNR 10 log db 10 MSE (1) MSE M 1 N 1 2 y, j o, j 0 j 0 M. N (2) PSNR values of denosed mage of varous methods at dfferent nose densty are detaled n table 1 usng standard lena mage. It s clear from the results that lower the nose densty, hgher PSNR value. PSNR values are lnearly decreasng order wth respect to ncreasng nose level. PSNR value of all methods n table 1 s also calculated n another mage gold hll mage. In both table 1 and table 2 FIRE and DWM have greater sgnal to nose rato correspondng to all other methods. Table 1: Performance Comparsons of PSNR Value n Lena Image FIRE [14] BDND [15] DWM [16] LUO [17] FMEM [18] SAM [19] CURRENT METHOD Mean Square Error MSE of resultant denosed mage by use of dfferent flterng methods n standard lena mage s tabulated n table 3. Unlke PSNR, MSE values are ncreasng lnearly for correspondng nose densty. DWM and current methods have better MSE at hgher nose level. Table 4 also descrbes the MSE values for all method n prevous table n gold hll mage. In gold hll mage LUO and current methods have better result. URL: 102

5 T r a n s a c t o n s o n M a c h n e L e a r n n g a n d A r t f c a l I n t e l l g e n c e V o l u m e 2, I s s u e 6, D e c Table 2: Performance Comparsons of PSNR Value n Gold Hll Image FIRE [14] BDND [15] DWM [16] LUO [17] FMEM [18] SAM [19] TLIDE PROPOSED Table 3: Comparson of MSE Value n Lena Image FIRE [14] BDND [15] DWM [16] LUO [17] FMEM [18] SAM [19] TLIDE PROPOSED Table 4: Comparson of MSE Value n Gold Hll Image FIRE [14] BDND [15] DWM [16] LUO [17] FMEM [18] SAM [19] TLIDE PROPOSED PSNR, MSE and run tme (RT) value of proposed method s descrbed n table 5(a) n standard lena mage. Hghest PSNR value s whch s better compare to all other methods as n lterature. MSE value should be as possble as smaller, s 1.61 at 10% nose n lena mage. Column 4, RT shows tme requred n mle seconds to remove mpulse nose from the nosy mage. Proposed method requres 135 msec at 40% nose densty. Table 5(b) presents correspondng PSNR, MSE and RT values n gold hll mage. In order to nose densty, PSNR s lnearly decreasng and MSE s n ncreasng order. C o p y r g h t S o c e t y f o r S c e n c e a n d E d u c a t o n U n t e d K n g d o m 103

6 Mthlesh Atulkar,A.S. Zadgaonkar and Sanjay Kumar; Impulse Nose Removal Technque Based on Fuzzy Logc, Transactons on Machne Learnng and Artfcal Intellgence, Volume 2 No 6 Dec (2014); pp: Table 5: Determnaton of the PSNR, MSE & RT Values n (a) Lena Image, (b) Gold Hll Image. Nose level PSNR MSE RT(msec) Nose level PSNR MSE RT(msec) Concluson In ths paper we present fuzzy logc based nose removal, detal preservng restoraton method. Ths s the ultmate flter for removal of mpulse nose. Even at very hgh nose densty mage detals texture and edges are preserved. The proposed method for mage enhancement s suffcent but the future work s to develop a method for mage enhancement n RGB color mage and vdeo sequenced mage. REFERENCES [1]. A. Bovk, Handbook of Image and Vdeo Processng. New York: Academc, [2]. Raymond H. Chan, Chung-Wa Ho and Mla Nkolova, Salt-and-Pepper Nose Removal by Medan-Type Nose Detectors and Detal-Preservng Regularzaton, IEEE Trans. Image Process. October (10): p DOI: /TIP [3]. Tuan-Anh Nguyen, Won-Seon Song, and Mn-Cheol Hong, Spatally Adaptve Denosng Algorthm for a Sngle Image Corrupted by Gaussan Nose, IEEE Trans. Consumer Electron. August (3): p [4]. R.C. Harde, C.G. Boncelet, LUM flters: a class of rank-order-based flters for smoothng and sharpenng, IEEE Trans. Sgnal Process., : p [5]. S.J. Ko, Y.H. Lee, Center weghted medan flters and ther applcatons to mage enhancement, IEEE Trans. Crcuts Syst., vol. 38, pp , [6]. S. Schulte, V. De Wtte, M. Nachtegael, D. Van der Weken, E.E. Kerre, Fuzzy random mpulse nose reducton method, Fuzzy Sets Syst., : p [7]. S. Schulte, M. Nachtegael, V. De Wtte, D. Van der Weken, E.E. Kerre, A fuzzy mpulse nose detecton and reducton method, IEEE Trans. Image Process., (5): p [8]. S. Schulte, S. Morllas, V. Gregor, E.E. Kerre, A new fuzzy color correlated mpulse nose reducton method, IEEE Trans. Image Process., (10): p [9]. S. M. M. Rahman, M. O. Ahmad, and M. N. S. Swamy, Vdeo denosng based on nter-frame statstcal modellng of wavelet coeffcents, IEEE Trans. Crcuts Syst. Vdeo Technol., (2): p URL: 104

7 T r a n s a c t o n s o n M a c h n e L e a r n n g a n d A r t f c a l I n t e l l g e n c e V o l u m e 2, I s s u e 6, D e c [10]. L. Jovanov, A. Pzurca, V. Zlokolca, S. Schulte, P. Schelkens, A. Munteanu, E. E. Kerre, and W. Phlps, Combned wavelet-doman and moton-compensated vdeo denosng based on vdeo codec moton estmaton methods, IEEE Trans. Crcuts Syst. Vdeo Technol., (3): p [11]. H. B. Yn, X. Z. Fang, Z. We, and X. K. Yang, An mproved moton-compensated 3-D LLMMSE flter wth spato-temporal adaptve flterng support, IEEE Trans. Crcuts Syst. Vdeo Technol., (12): p [12]. T. Mélange, M. Nachtegael, E. E. Kerre, V. Zlokolca, S. Schulte, V. De Wtte, A. Pzurca, and W. Phlps, Vdeo denosng by fuzzy moton and detal adaptve averagng, J. Electron. Imagng, (4). [13]. Tom Mélange, Mke Nachtegael and Etenne E. Kerre, Fuzzy Random Impulse Nose Removal From Color Image Sequences, IEEE Trans. Image Process., (4): p , DOI: /TIP [14]. F. Russo and G. Rampon, A fuzzy flter for mages corrupted by mpulse nose, IEEE Sgnal Process. Lett., june (6): p [15]. P. E. Ng and K. K. Ma, A swtchng medan flter wth boundary dscrmnatve nose detecton for extremely corrupted mages, IEEE Trans. Image Process., June (6): p [16]. Y. Dong and S. Xu, A new drectonal weghted medan flter for removal of random-valued mpulse nose, IEEE Sgnal Process. Lett., Mar (3): p [17]. W. Luo, An effcent algorthm for the removal of mpulse nose from corrupted mages, AEU- Int. J. Electron. Commun, : p [18]. J. Zhang, An effcent medan flter based method for removng random valued mpulse nose, Dgt. Sgnal Process, July (4): p [19]. H. Ibrahm, N. S. P. Kong, and T. F. Ng, Smple adaptve medan flter for the removal of mpulse nose from hghly corrupted mages, IEEE Trans. Consumer Electron., Nov (4): p C o p y r g h t S o c e t y f o r S c e n c e a n d E d u c a t o n U n t e d K n g d o m 105