IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER

Transcription

1 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Suman Shrestha 1, 2 1 Unversty of Massachusetts Medcal School, Worcester, MA Department of Electrcal and Computer Engneerng, The Unversty of Akron, Akron, OH ABSTRACT Nose s a major ssue whle transferrng mages through all knds of electronc communcaton. One of the most common nose n electronc communcaton s an mpulse nose whch s caused by unstable voltage. In ths paper, the comparson of known mage denosng technques s dscussed and a new technque usng the decson based approach has been used for the removal of mpulse nose. All these methods can prmarly preserve mage detals whle suppressng mpulsve nose. The prncple of these technques s at frst ntroduced and then analysed wth varous smulaton results usng MATLAB. Most of the prevously known technques are applcable for the denosng of mages corrupted wth less nose densty. Here a new decson based technque has been presented whch shows better performances than those already beng used. The comparsons are made based on vsual apprecaton and further quanttatvely by Mean Square error (MSE) and Peak Sgnal to Nose Rato (PSNR) of dfferent fltered mages.. KEYWORDS Impulse Nose, Nonlnear flter, Adaptve Flters, Decson Based Flters. 1. INTRODUCTION The goal of ths study s to examne effcent and relable mage denosng algorthms of mpulsve nose. Two algorthms, adaptve flterng technques and decson based flterng technques, have been explaned n detal whle other algorthms have been used for smulaton purposes and explaned n the revew of medan flters n Secton 2. Further, a technque known as adaptve decson based flterng s proposed and appled whch s a new technque for the removal of mpulse nose. It s then compared wth the prevous technques wth respect to ther performance. Images are always preferred to texts n multmeda transmsson but all these communcatons face a common problem: "Nose". One of the most common form of nose s the mpulse nose, also known as salt and pepper nose whch s caused by unstable voltage whch s due to transmsson or errors generated n the communcaton channel. The mpulse nose produces fxed values n the pxels whch are 0 (pepper nose) and 255 (salt nose). The nose model for an mpulse nose can be expressed as: 0, wth probablty pn x = 255, wth probablty pp φ, wth probablty 1 ( pn + p p ) (1) DOI : /spj

2 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 where x denotes the pxel of corrupted mage, φ denote the pxel, pn and of the pxel corrupted wth pepper nose and salt nose respectvely, where 1/2*Nose Rato, where Nose rato les between 0 and 1. pp are the probablty pn and p p = Nose flterng technques can ether be lnear or non-lnear. The lnear flterng technque apples the algorthm lnearly to all the pxels n the mage wthout defnng the mage as corrupted or uncorrupted pxel. Snce the algorthm apples to all the pxels n the mage, so ths causes the uncorrupted pxels to be fltered and hence these flterng technques are not effectve n removng mpulsve noses. On the other hand, non-lnear flterng technque [1]-[2] s a two phase flterng process. In the frst phase, the pxels are dentfed as corrupted or uncorrupted pxel and n the second phase, the corrupted pxel s fltered usng the specfed algorthm whle the uncorrupted pxel value s retaned. The most wdely used non-lnear flter s the medan flter whch uses the medan value to replace the corrupted pxel, and these flters have the capablty to remove mpulsve nose whle preservng the edges. A dagram hghlghtng the prncple of non-lnear flters s shown n Fgure 1. Fgure 1. Non-lnear Flterng Technque. Many researchers have been workng on dfferent non-lnear flterng technques. In Secton 2, some of these flterng technques wll be dscussed lke the standard medan flter (SM), centerweghted medan flter (CWMF) [3], tr-state medan flter (TSMF [4], progressve swtchng medan flter (PSMF) [5] and adaptve progressve swtchng medan flter (APSMF) [6]. In Secton 3, two of the currently known best non-lnear flterng technques, the conventonal adaptve flterng technque (AMF) and the decson based medan flterng technque (DBMF) wll be explaned. In Secton 4, a new decson based medan flter known as the adaptve decson based medan flter (whch s dentfed as the combnaton of the adaptve flter and decson based flter) wll be proposed. The smulaton results wll be shown and dscussed n Secton 5 and the conclusons wll be drawn n Secton 6. In ths paper, the performance evaluaton of the smulated results s carred out by calculatng the Peak Sgnal to Nose Rato of the fltered mage. The peak sgnal to nose rato of an mage s gven by: PSNR = 10* log10 (2) MSE where, MSE s the mean square error and s gven by: M N 1 2 MSE = [ I(, j) I (, j)] (3) MN = 1 j = 1 2

3 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Here, M and N are the horzontal and the vertcal dmensons of the mage; I s the orgnal uncorrupted mage and I s the fltered mage. 2. REVIEW OF MEDIAN FILTERS The basc medan flter s the standard medan flter. In ths method, a square wndow of sze 2k+1, where k goes from 1 to N, s used to flter the center pxel. The pxels n the wndow are frst sorted and the center pxel s changed to the medan value of the sorted sequence. Ths method s the smplest of the medan flterng technques and because of ts smplcty; t has been used for a long tme. The second type of medan flterng s the center weghted medan flterng technque. Ths s smlar to the standard medan flterng technque except that the center pxel n the wndow to be fltered s assgned a certan weght,.e. the center pxel s repeated for some number of tmes whch s defned as the weght. Snce the center pxel n the wndow to be fltered s repeated for some number of tmes, ths technque s called the center weghted medan flter. Let, I j be the center pxel of the wndow W, then the output (fltered) pxel of the center weghted medan flter as gven by [3] s: I = medan[ I, w I ( s, t) W,( s, t) (0,0)] (4) j s, j t j where, W s the wndow, w s the weght of the center pxel, tmes n calculatng the medan. w Ij denotes that Ij s repeated w The next medan flter used to remove mpulsve nose s the tr-state medan flter. It uses the standard medan flter's and the center weghted medan flter's outputs to develop a new flterng technque. The algorthm as developed by \cte{4} s gven by: Ij, TD d1 TSM CWM Ij = Ij, d T < d SM Ij, TD < d2 2 D 1 (5) Here CWM Ij and SM Ij are the outputs of the center weghted medan flter and the standard medan SM CWM flter, d 1 = Ij Ij and d 2 = Ij Ij and T D s the threshold used whch s between 0 and 255. The medan flterng technque, whch uses a more complex technque to detect the mpulse noses and then flter the pxels, s the progressve medan flterng technque [5]. Ths technque uses an mpulse detector technque to detect the pxels corrupted wth mpulse nose and then uses a flterng technque to flter these corrupted pxels. Both these detecton and flterng technques are appled through several teratons to ensure that all the nosy pxels have be detected (n the case when the mage s hghly corrupted) and the corrupted pxels have been fltered usng ths method. The two steps n ths method are:. Impulse Nose Detecton: Ths step produces two mage sequences, one the pxel values of the mage after ND teratons and the other whch ndcates whether the pxel s corrupted or uncorrupted. The second mage sequence s named as a flag sequence. 3

4 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Intally, all the pxel values of the flag sequence s assgned as 0 whch ndcates that the pxel value s uncorrupted. Once the detecton algorthm s appled, the flag sequence s changed to 1 f the pxel s detected as nose and s unaltered f t s a nose-free pxel. ( n 1) ( ) Let, be the mage sequence after (n-1) teraton and I n be the sequence after n I teratons, ( n 1) f be the flag sequence after (n-1) teratons and f be the flag sequence after n teratons. A wndow (W) s specfed to calculate the medan value of the pxel. The medan value of the correspondng pxel of the mage sequence s gven by: ( n 1) ( n 1) ( n ) med = medan[ I W ] (6) The mpulse nose s then detected usng med ( n 1) and ( n 1) I and s gven by: < = (7) 1, otherwse ( n 1) ( n 1) ( n 1) ( n) f, f I med TD f Here, T D s a predefned threshold. Usng ths, the value of each of the pxel s then modfed as (to be used for next teraton): ( n 1) ( n) ( n 1) ( n) m, f f f I = (8) ( n 1) ( n) ( n 1) I, f f = f Ths s repeated for ND teratons and gves two mage sequences: the fnal mage sequence and the flag sequence.. Flterng Phase: Ths phase flters the corrupted pxels obtaned from the frst phase. It uses two mage sequences to flter the mage, one s the flag sequence from the frst phase 0 ( ND ) and the other s the corrupted pxel sequence. Let, g = f be the flag sequence and 0 I be the corrupted mage sequence. A wndow (W) s specfed to calculate the medan th value of the pxel. For the N teraton, the medan value of the mage sequence from the specfed wndow s calculated usng only the good pxel values,.e., med = medan[ I g = 0, W ] (9) ( n 1) ( n 1) ( n 1) After calculatng the medan value, the mage sequence and the flag sequence are modfed as: I m, f g = 1; M > 0 = (10), otherwse ( n 1) ( n 1) ( n) ( n 1) I where, M s the number of uncorrupted pxels n the wndow. If the corrupted pxel s modfed, the flag sequence s modfed to 0. Thus, g g, f I = I ( n 1) ( n) ( n 1) ( n) = ( n) ( n 1) 0, f I = med (11) 4

5 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Ths process s repeated untl all the pxels of the flag sequence have been changed to 0. Fnally, t gves the fltered mage I N j, where N s the number of repetton of the flterng technque. A modfcaton of the progressve swtchng flterng technque s the adaptve progressve swtchng flterng technque [6]. Ths method also flters the mage n two phases smlar to the prevous one. The mpulse nose detecton phase s smlar to progressve swtchng technque except for Equaton 7 n detectng the nose whch s appled as: f, f I med < T ; ( n 1) ( n 1) ( n 1) D ( n) ( n 1) ( n 1) ( n 1) = < < f mn( I ) medan max( I ) 1, otherwse (12) The nose flterng phase s also smlar to the prevous method except t uses adaptve method to flter the nosy pxels. Adaptve mples that the wndow sze s ncreased f a certan condton does not meet whle flterng the pxel. Ths means a slght modfcaton s requred n Equaton 10. The medan flterng technque s appled f the number of uncorrupted pxels n the wndow s greater or equal than half the number of pxels n the wndow and the wndow sze s less than the maxmum specfed wndow sze (whch s to ensure the correct value of the pxel s obtaned) else the wndow sze s ncreaed by 2 n each of the horzontal and vertcal sde and the process s repeated,.e. Equaton 10 s modfed to: I ( n) ( n 1) ( n 1) m, f g = 1; M > 0.5*( W * W ); = W < Wmax W + 2, otherwse (13) Here, M s the number of uncorrupted pxels n the wndow and W max s the maxmum wndow sze (predefned) that can be appled. The other steps are smlar to the prevous method (Progressve Swtchng Flterng) descrbed above. Ths s appled untl all the corrupted pxels have been fltered. 3. CONVENTIONAL ADAPTIVE MEDIAN FILTERING AND DECISION BASED MEDIAN FILTERING A. Conventonal Adaptve Medan Flter [7]: The adaptve medan flter also apples the nose detecton and flterng algorthms to remove mpulsve nose. The sze of the wndow appled to flter the mage pxels s adaptve n nature,.e. the wndow sze s ncreased f the specfed condton does not meet. If the condton s met, the pxel s fltered usng the medan of the wndow. Let, I j be the pxel of the corrupted mage, I mn be the mnmum pxel value and I max be the maxmum pxel value n the wndow, W be the current wndow sze appled, W max be the maxmum wndow sze that can be reached and I med be the medan of the wndow assgned. Then, the algorthm of ths flterng technque completes n two levels as descrbed: 5

6 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Level A: a) If I mn < I med < I max, then the medan value s not an mpulse, so the algorthm goes to Level B to check f the current pxel s an mpulse. b) Else the sze of the wndow s ncreased and Level A s repeated untl the medan value s not an mpulse so the algorthm goes to Level B; or the maxmum wndow sze s reached, n whch case the medan value s assgned as the fltered mage pxel value. Level B: a) If I mn < I j < I max, then the current pxel value s not an mpulse, so the fltered mage pxel s unchanged. b) Else the mage pxel s ether equal to I max or I mn (corrupted), then the fltered maged pxel s assgned the medan value from Level A. These types of medan flters are wdely used n flterng mage that has been denosed wth nose densty greater than 20%. B. Decson Based Medan Flterng [8]: The decson based medan flterng method processes the corrupted mage pxels by frst detectng whether the pxel value s a corrupted one. Ths decson s made smlar to the one made for adaptve medan flterng technque,.e. based on whether the pxel value to be processed les between the mnmum and the maxmum value nsde the wndow to be processed. If the pxel value les between the mnmum and the maxmum value n the wndow, the pxel s left unchanged (as t s detected as a nose free pxel), otherwse the pxel s replaced wth the medan value of the wndow or ts neghbourhood pxel value. The complete algorthm of the decson based flterng technque s as stated below: Step A: A wndow s selected and the mnmum, maxmum and the medan value of the wndow are detected as the frst step. Let I j be the pxel value, I max, I mn and I med be the maxmum pxel value, mnumum pxel value and medan pxel value respectvely n the wndow. Step B: a) If I mn < I j < I max, then the pxel value s not an mpulse nose and hence the same pxel value s retaned n the fltered mage else the pxel value s an mpulse nose. b) If I j s an mpulse nose, then t s checked to see f the medan value s an mpulse nose or not. If I mn < I med < I max or 0 < I med < 255, the medan s not an mpulse nose and the fltered mage pxel s replaced by the medan value of the wndow. c) If the medan s also an mpulse nose, and n ths case the fltered mage pxel s replaced by the value of the left neghbourhood pxel value (whch has already been fltered). Step C: These steps are repeated untl all the pxels have been tested. 6

7 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August PROPOSED METHOD A new algorthm (Adaptve Decson Based Medan Flterng Algorthm) s mplemented to see f the performance of the mage s ncreased wth respect to the decson based medan flterng. Ths algorthm seems to be smlar to adaptve medan flterng technque. A small change s made to ths wth respect to the decson based medan flter and adaptve medan flter. The change s made n Step B (c) wth respect to decson based medan flterng and at the end wth respect to adaptve medan flterng technque. If the medan s also an mpulse nose, the wndow sze s ncreased by 2 n both horzontal and vertcal drecton (change made wth respect to decson based medan flter) and the same algorthm s repeated to see f the new medan obtaned after ncreasng the wndow sze s an mpulse nose. If t s an mpulse nose agan, the same algorthm s repeated untl the maxmum sze of the wndow s reached; otherwse the fltered mage pxel s replaced by the left neghbourhood pxel value as n the decson based medan flterng (change made wth respect to adaptve medan flter). Ths new algorthm s appled to see f any better flterng of the corrupted mage could be acheved as t s known that adaptve based algorthm provdes better flterng than that appled wthout adaptve medan flterng technque. 5. SIMULATION RESULTS AND DISCUSSIONS The above mentoned algorthms are all mplemented n two mages of sze 512 x 512 pxels: the Lena mage and the Boat mage as shown n the Fgures 2 and 3. Impulse noses are added to the mages and ther performances are evaluated. The nose denstes from 5% to 50% are added to the mages and ther MSE and PSNR are calculated. The PSNR and MSE are calculated from Equatons 2 and 3 respectvely. It s to be noted that greater the value of PSNR and lower the value of MSE, the flterng technque s better. These results are presented n a tabular form n Table 1 and 2. Table 1. PSNR values for Lena Image at dfferent nose denstes Table 2. PSNR values for Boat Image at dfferent nose denstes 7

8 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Fgure 2. Smulaton Results for Lena Image at 50% nose densty (a) Orgnal Image, (b) Nosy Image, (c) Standard Medan Flterng, (d) Center Weghted Medan Flterng, (e) Tr-State Medan Flterng, (f) Progressve Swtchng Medan Flterng, (g) Adaptve Progressve Swtchng Medan Flterng, (h) Adaptve Medan Flterng, () Decson Based Medan Flterng, (j) Adaptve Decson Based Medan Flterng Also for the three algorthms descrbed n Secton 3 and the proposed method n Secton 4, the nose denstes are ncreased from 60% to 90% for the two mages and the performances are evaluated. The results of PSNR for these two mages are presented n Table 3. The results presented n the tables suggest that adaptve medan flterng and decson based medan flterng perform better than other technques. The flterng technques lke standard medan flterng, center weghted medan flterng and tr-state medan flterng (all performed for 3 x 3 wndow) show good performances only for low nose densty. The performances for these flters could be ncreased f the wndow sze s ncreased, but as the wndow sze ncreases, the mage tends to show blurrng effect. The other flterng technques lke progressve swtchng medan flterng and adaptve progressve swtchng medan flterng show consderably better performance than the above mentoned three flterng technques at nose densty up to 50%. But as the nose densty ncreases further, these technques lead to lower performance. These two flterng technques also show consderably low performance f there are salt nose on a whte background and pepper nose on a black background n the orgnal mage. Table 3. PSNR values for Boat and Lena Image at hgher nose denstes for Adaptve, Decson Based and Adaptve Decson Based Flter These effects can be seen n Fgures 2 and 3 that PSMF fals to remove salt nose from the whte background (Fgure 2(f)) and pepper nose from the black background (Fgure 3(f)). Ths effect s low (but stll exsts somewhat) n APSMF (Fgures 2(g) and 3(g)). 8

9 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Fgure 3. Smulaton Results for Boat Image at 30% nose densty (a) Orgnal Image, (b) Nosy Image, (c) Standard Medan Flterng, (d) Center Weghted Medan Flterng, (e) Tr-State Medan Flterng, (f) Progressve Swtchng Medan Flterng, (g) Adaptve Progressve Swtchng Medan Flterng, (h) Adaptve Medan Flterng, () Decson Based Medan Flterng, (j) Adaptve Decson Based Medan Flterng When compared to these fve flterng technques, conventonal adaptve medan flterng and decson based medan flterng show better performances overall. The performance also somewhat depends on the mage that s beng analyzed. In the Lena mage, adaptve progressve medan flterng shows better performance than the conventonal adaptve medan flterng technque for nose densty up to 50% based on PSNR calculaton, but based on the vsual effect, the adaptve medan flterng has better results. Ths s because of the salt nose whch s yet not fltered on adaptve progressve medan flterng technque on the whte background. But f the Boat mage s used, whch has more dark spots, the adaptve progressve swtchng technque shows degradaton n the performance than the adaptve medan flterng technque. Also the man dsadvantages of the adaptve progressve swtchng as compared to the tradtonal adaptve medan technque s that t takes relatvely hgh computaton tme and s not very well suted for real tme applcatons. On the other hand, decson based medan flterng shows the best performance for all nose denstes. Upon analyss, t s found that ths technque can flter the mpulsve nose for denstes up to 60% wthout any blurrng effect on the fltered mage. But for larger nose denstes, although t produces the best results out of the mentoned technques, the blurrng of the fltered mages tends to ncrease. Ths effect s however a lttle less f the proposed technque called adaptve decson based medan flterng s used. Ths technque shows performance smlar to decson based medan flterng wth nose denstes up to 50%, but as the nose densty ncreases further, the adaptve technque performs better than the decson based technque based on both vsual clarty and PSNR calculaton. Thus, based on the analyss presented here, t s clear that adaptve flterng technque shows better performance than the flterng done wthout any adaptve technque. Ths can be seen wth the comparson of Progressve swtchng technque to the same technque used wth adaptve form and also of the decson based technque to ts adaptve form. 9

10 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 (a) (b) Fgure 4. Plot of MSE for dfferent medan flterng technques for Boat mage (a) MSE Plot for all medan flterng technques (b) MSE plot for three technques dscussed n Secton 3. When all these flterng technques are compared, t can be concluded that all these use fxed or varable wndow sze for removal of mpulse nose. There s stll a need for an algorthm whch could automatcally decde the wndow sze based on the mage and nose densty level. Further, the plot of the calculated PSNR and MSE values of the dscussed technques are presented n 10

11 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Fgures 4 and 5 (only Boat mage) to clarfy the dscussons. (a) (b) Fgure 4. Plot of PSNR for dfferent medan flterng technques for Boat mage (a) PSNR Plot for all medan flterng technques (b) PSNR plot for three technques dscussed n Secton 3. In addton to these adaptve medan flters, there s also a robust estmaton based flter for the removal of hgh nose densty as proposed n [11]. The smulaton results provded n the paper can also be appled n hardware and the real mplementaton of the algorthms could be developed as presented n dfferent lteratures [9]-[10]. 11

12 Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August CONCLUSIONS Dfferent medan flterng technques have been dscussed and a new technque s proposed whch produces the best results among all the technques avalable. The results presented above reveal that decson based medan flterng technque (among the prevous technques) gves better performance for mage denosng of mpulsve nose based on PSNR and vsual clarty. For very hgh nose densty, adaptve decson based medan flterng could be used to get better vsual clarty and PSNR values than the decson based medan flterng technque. Conventonal adaptve medan flterng too gves good vsual clarty whle denosng mpulsve nose for nose denstes up to 50%. However, n comparson to these technques, progressve swtchng and adaptve progressve swtchng exhbt good performance for low and medum nose denstes but takes more computaton tme and hence s not applcable for real tme applcatons. Thus, as a whole, decson based medan flterng technque and adaptve decson based medan flterng technque are better technques than any other technques descrbed above for flterng mpulse noses and the computaton tme for these technques are consderably less makng them the deal technque for use n real tme applcaton. However for hgher nose densty, the proposed technque exceeds the decson based technque based on performance. But f the nose denstes are consderably hgher, then new algorthms must be developed to get much better results than all the technques descrbed n ths paper. ACKNOWLEDGEMENTS The author would lke to thank Prof. Dr. S.I. Harharan (The Unversty of Akron) for hs valuable nput n ths work. Ths work was performed whle the author was pursung hs graduate studes at the Unversty of Akron. REFERENCES [1] I. Ptas and A. N. Venetsanopoulos, Nonlnear Dgtal Flters: Prncples and Applcatons, Boston, MA: Kluwer, [2] J. Astola and P. Kuosmanen, Fundamentals of Nonlnear Dgtal Flterng, CRC Press, [3] T. Sun, M. Gabbouj and Y. Neuvo, Center weghted medan flters: Some propertes and ther applcatons n mage processng, Sgnal Processng, vol. 35, Issue 3, pp , February [4] T. Chen, K. K. Ma, L.H. Chen, Tr-State Medan Flter for Image Denosng, IEEE Transactons on Image Processng, vol. 8, Issue 12, pp , [5] Z. Wang, D. Zhang, Progressve swtchng medan flter for the removal of mpulse nose from hghly corrupted mages, IEEE Transactons on Crcuts and Systems, vol. 46, Issue 1, pp 78-80, Jan [6] V. V. Khryashchev, A. L. Prorov; I. V. Apalkov, P. S. Zvonarev, Image denosng usng adaptve swtchng medan flter, IEEE Internatonal Conference on Image Processng, vol. 1, pp , [7] Y. Zhao, D. L, Z. L, Performance enhancement and analyss of an adaptve medan flter, Internatonal Conference on Communcatons and Networkng, pp , [8] V. Backman, R. Gurjar, K. Badzadegan, I. Itzkan, R. R. Dasar, L.T. Perelman and M.S. Feld, A New Fast and Effcent Decson-Based Algorthm for Removal of Hgh-Densty Impulse Noses, Sgnal Processng Letters, IEEE, Vol. 14, Issue 3, pp , 2007 [9] Z. Vascek and L. Sekanna, Novel Hardware Implementaton of Adaptve Medan Flters, Desgn and Dagnostcs of Electronc Crcuts and Systems, IEEE, pp 1-6, Aprl [10] B. Carolne, G. Sheeba, J. Jeyaran, F. Salma Roslne Mary, VLSI mplementaton and performance evaluaton of adaptve flters for mpulse nose removal, Emergng Trends n Scence, Engneerng and Technology (INCOSET), 2012 Internatonal Conference on, vol., no., pp.294,299, Dec [11] V.R. Vjaykumar, P.T. Vanath, P. Kanagasabapathy and D. Ebenezer, Hgh Densty Impulse Nose Removal Usng Robust Estmaton Based Flter, IAENG Internatonal Journal of Computer Scence, August

13 AUTHOR Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 Suman Shrestha receved hs B. E. n Electroncs and Communcaton Engneerng n 2009 from Trbhuvan Unversty, Kathmandu, Nepal and hs M.S. degree n Electrcal Engneerng from the Unversty of Akron. He s currently workng as a Research Engneer n the Radologc Physcs Laboratory at the Unversty of Massachusetts Medcal School. Hs work s focused n the desgn of new X-ray detectors for use n Medcal Imagng and Radotherapy. Hs research nterests are n the feld of Polarmetry, Image Processng, Medcal Imagng and Radaton and X-ray detectors. 13