一种基于区域自适应的非局部均值(Nonlocal Means)图像去噪方法

Region-based non-local means algorithm for noise removalW.L.Zeng and X.B.LuThe non-local means (NLM)provides a useful tool for image denoising and many variations of the NLM method have been proposed.However,few works have tried to tackle the task of adaptively choos-ing the patch size according to region characteristics.Presented is a region-based NLM method for noise removal.The proposed method first analyses and classifies the image into several region types.According to the region type,a local window is adaptively adjusted to match the local property of a region.Experimental results show the effectiveness of the proposed method and demonstrate its superior-ity to the state-of-the-art methods.Introduction:The use of the non-local means (NLM)filter for noise removal has been extensively studied in the past few years.The NLM filter was first addressed in [1].The discrete version of the NLM is as follows:u (k ,l )=(i ,j )[N (k ,l )w (k ,l ,i ,j )v (i ,j )(1)where u is the restored value at pixel (k,l )and N (k,l )stands for theneighbourhood of the pixel (k,l ).The weight function w (k,l,i,j )is defined asw (k ,l ,i ,j )=1exp −||T k ,l v −T i ,j v ||22,a(2)where T k,l and T i,j denote two operators that extract two patches of sizeq ×q centred at pixel (k,l )and (i,j ),respectively;h is the decay para-meter of the weights; . 2,a is the weighted Euclidean norm using a Gaussian kernel with standard deviation a ,and Z (k,l )is the normalised constantZ (k ,l )= (i ,j )exp −||T k ,l v −T i ,j v ||22,ah 2(3)The core idea of the NLM filter exploits spatial correlation in the entireimage for noise removal and can produce promising results.This method is time consuming and not able to suppress any noise for non-repetitive neighbourhoods.Numerous methods were proposed to accel-erate the NLM method [2–4].Also,variations of the NLM method have been proposed to improve the denoising performance [5–7].In smooth areas,a large matching window size could be used to reduce the influ-ence of misinterpreting noise as local structure.Conversely,a small matching window size could be used for the edge /texture region,which means not only the local structure existing within a neighbour-hood can be effectively used but can also speed up the matching process.To the best of our knowledge,few works have tried to tackle the task of adaptively choosing the patch size according to region characteristics.To overcome the disadvantage of the NLM method and its variances,in this Letter we present an adaptive NLM (ANLM)method for noise removal.The proposed method first analyses and classifies the image into several region types based on local structure information of a pixel.According to the region type,a local window is adaptively adjusted to match the local property of a region.Experimental results show the effectiveness of the proposed method.Proposed NLM algorithm:The adaptive patches based non-local means algorithm is conducted according to the region classification results,owing to the fact that the structure tensor can obtain more local structure information [8].Therefore,we use it to classify the region.For each pixel (i,j )of the region,the structure tensor matrix is defined asT s =t 11t 12t 12t 22 =G s ∗(g x (i ,j ))2G s ∗g x (i ,j )g y (i ,j )G s ∗g y (i ,j )g x (i ,j )G s ∗(g y (i ,j ))2where g x and g y stand for gradient information in the x and y directions,G s denotes a Gaussian kernel with a standard deviation s .Theeigenvalues l 1and l 2of T s are given byl 1=12t 11+t 22+ (t 11−t 22)2+4t 212 and l 2=1t 11+t 22− (t 11−t 22)2+4t 212 For a pixel in the smooth region,there is a small eigenvalue difference;for a pixel in an edge /texture region,there is a large eigenvalue differ-ence.Therefore,region classification can be achieved by examining the eigenvalue difference of each pixel.Let l (i ,j )=|l 1(i ,j )−l 2(i ,j )|.We propose the following classifi-cation scheme to partition the whole image region into n classes {c 1,···,c n }:(i ,j )[c 1,if l (i ,j )≤l min +(l max −l min )n c 2,if l (i ,j )≤l min +2(l max −l min )n ...c n ,if l (i ,j )≤l min +n (l max −l min )n ⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎩where l min and l max are the minimum and maximum of {l (i ,j ):(i ,j )[V },respectively.To exploit the local structure information and reduce noise in different regions,we adaptively choose the matching window based on the region classification result.The scheme for selecting the matching window is asfollows:if (k ,l )[c r ,T k ,l :=T r k ,l ,where T rk ,l denotes an operator of the r-type region that extracts one patch of size q r ×q r .To reduce the influ-ence of misinterpreting noise as local structure,a larger patch size is adopted for a smooth region.In contrast,a small patch size is employed for the edge /texture region.Intuitively,the number of the class n should be as big as possible.In practice,the gain is insignificant for n greater than 4.Therefore,we choose n ¼4in our experiments.Table 1:PSNR performance comparison of ‘Lena’,‘Barbara’,‘Peppers’imagesFig.1Comparison of results with additive Gaussian noise of s ¼35a Original image b Noisy image c NLM d WUNLM e ANLMExperimental results:In this Section,we compare our proposed ANLM method with the NLM method [2]and the weight update NLM (WUNLM)method [3].We test the proposed method on ‘Lena’,‘Barbara’,and ‘Peppers’,which were taken from the USC-SIPI Image Database (/database/base).The performance of the method was evaluated by measuring the peak signal-to-noise ratio (PSNR).In general h corresponds to the noise level and is usuallyELECTRONICS LETTERS 29th September 2011Vol.47No.20,1125-1127fixed to the standard deviation of the noise.The size of the search window is21×21.Table1shows results obtained with three methods across four noise levels.Figs.1a and b,show the‘Barbara’image and the corresponding noisy image generated by adding Gaussian white noise with variance s¼35,respectively.Figs.1c–e show denoised images by using the NLM,WUNLM,and ANLM methods,respectively.From the standpoint of perceptual view and PSNR values,the proposed ANLM method produced the best quality. Conclusions:An adaptive NLM(ANLM)method for noise removal is presented.In the method,an image isfirst analysed and classified into several region types.According to the region type,a local window is adaptively adjusted to match the local property of a region. Experimental results show the effectiveness of the proposed method and demonstrate its superiority to the state-of-the-art methods. Acknowledgments:This work was supported by the National Natural Science Foundation of China under grant60972001,the National Key Technologies R&D Program of China under grant2009BAG13A06 and the Scientific Innovation Research of College Graduate in Jiangsu Province under grant CXZZ_0163.#The Institution of Engineering and Technology20115August2011doi:10.1049/el.2011.2456W.L.Zeng(School of Transportation,Southeast University,Nanjing 210096,People’s Republic of China)X.B.Lu(School of Automation,Southeast University,Nanjing210096, People’s Republic of China)E-mail:xblu2008@References1Budades,A.,Coll,B.,and Morel,J.M.:‘A review of image denoising algorithms,with a new one’,Multiscale Model Simul.,2005,4,(2), pp.490–5302Mahmoudi,M.,and Sapiro,G.:‘Fast image and video denoising via nonlocal means of similar neighborhoods’,IEEE Signal Process.Lett., 2005,12,(12),pp.839–8423Vignesh,R.,Oh,B.T.,and Kuo,C.-C.J.:‘Fast non-local means(NLM) computation with probabilistic early termination’,IEEE Signal Process.Lett.,2010,17,(3),pp.277–2804Brox,T.,Kleinschmidt,O.,and Cremers,D.:‘Efficient nonlocal means for denoising of textural patterns’,IEEE Trans.Image Process.,2008, 17,(7),pp.1083–10925Kervrann,C.,and Boulanger,J.:‘Optimal spatial adaptation for patch-based image denoising’,IEEE Trans.Image Process.,2006,15,(10), pp.2866–28786Ville,D.V.D.,and Kocher,M.:‘SURE-based non-local means’,IEEE Signal Process.Lett.,2009,16,(11),pp.973–9767Park,S.W.,and Kang,M.G.:‘NLM algorithm with weight update’, Electron.Lett.,2010,16,(15),pp.1061–10638Brox,T.,Weickert,J.,Burgeth,B.,and Mrazek,P.:‘Nonlinear structure tensors’,Image put.,2006,24,pp.41–55ELECTRONICS LETTERS29th September2011Vol.47No.20。