Neural Network Based Noise Identification in Digital Images

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Image noise is unwanted information in an image
and can occur at any moment of time such as during image
capture, transmission, or processing and it may or may not
depend on image content. In order to remove the noise from
the noisy image, prior knowledge about the nature of noise
must be known otherwise noise removal causes the image
blurring. Identifying nature of noise is a challenging problem.
Many researchers have proposed their ideas on image noise
identification and each of the work has its assumptions,
advantages and limitations. In this paper, we proposed a new
methodology based on neural network for identifying the
different types of noise such as Non Gaussian, Gaussian white,
Salt and Pepper and Speckle noise.

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Neural Network Based Noise Identification in Digital Images

  1. 1. ACEEE Int. J. on Network Security , Vol. 02, No. 03, July 2011 Neural Network Based Noise Identification in Digital Images Karibasappa K.G1, Shivarajkumar Hiremath2, K. Karibasappa3 1, B.V.B.College of Engg. &Tech.,Hubli-31 Email: Karibasappa_kg@bvb.edu 2 B.V.B.College of Engg. &Tech.,Hubli-31 Email: shivaraj2323@gmail.com 3 Dayanand Sagara College of Engineering and Tech., Bangalore Email: k_karibasappa@hotmail.comAbstract: Image noise is unwanted information in an image considerable interest, because once the type of noise isand can occur at any moment of time such as during image identified from the given image, an appropriate algorithm cancapture, transmission, or processing and it may or may not then be used to de-noise it. Also, since poor de-noising oftendepend on image content. In order to remove the noise from results from poor noise identification, a better noisethe noisy image, prior knowledge about the nature of noise identification technique is always preferred. In the literaturemust be known otherwise noise removal causes the imageblurring. Identifying nature of noise is a challenging problem. we found different image noise identification techniquesMany researchers have proposed their ideas on image noise namely noise identification techniques based on statisticalidentification and each of the work has its assumptions, parameters[5], noise identification techniques based on softadvantages and limitations. In this paper, we proposed a new computing approach[6] , noise identification techniquesmethodology based on neural network for identifying the based on graphical methods [7] and noise identificationdifferent types of noise such as Non Gaussian, Gaussian white, techniques based on gradient function methods [8]. In theSalt and Pepper and Speckle noise. literature we found different image noise identification techniques namely noise identification techniques based onIndex Terms— Image noise, PNN, kurtosis, skewness statistical parameters[5], noise identification techniques based on soft computing approach[6] , noise identification I. INTRODUCTION techniques based on graphical methods [7] and noise Image noise is the random variation of brightness or color identification techniques based on gradient function methodsinformation in images produced by the acquisition process [8].due to camera quality, acquisition condition, such as A. Probabilistic Neural Network(PNN)illumination level, calibration and positioning or it can be afunction of the scene environment. Presence of noise is Probabilistic Neural Networks (PNN) [9] is feed-forwardmanifested by undesirable information, which is not at all neural networks that can be used as general purposerelated to the image under study, but in turn disturbs the classifiers. PNNs were proposed by Specht in 1989, it is ainformation present in the image. So elimination of noise is type of Radial Basis Function (RBF) network which is suitableone of the key research work to be done in computer vision for pattern classification. The PNN classifier is basically aand image processing as noise leads to the error in the image. classifier, of which the network formulation is based on theAccordingly there are different categories of noise present probability density estimation of the input signals.such as gaussian noise, non gaussian noise, speckle noise Probabilistic Neural Networks (PNN) estimate the probabilityand salt-pepper noise, film grain noise, thermal Noise, density function for each class based on the training samples.photoelectron noise. Many papers are published to illustrate PNNs have gained attention because they offer a way tothe techniques for image noise identification and classification interpret the network’s structure in the form of a probability[1]. But most of the researchers have used the simple density function and their performance is often superior thanconventional method. Here we are proposing the technique other classifiers. Because of ease of training and a soundfor image noise identification using Probabilistic Neural statistical foundation in Bayesian estimation theory, PNNNetworks. A wide variety of image de-noising algorithms have has become an effective tool for solving many classificationappeared in the literature. In general, they perform quite well, problems. Finally, the problem is formulated to identify thebut almost all of them focus towards reduction or removal of type of noise from the observed image, where the main goala specific type of noise, such as, non-gaussian or gaussian is to identify the nature of noise present in images usingwhite noise [2], speckle noise [3] and impulsive (or, salt-and- probabilistic neural network.pepper) noise [4]. Although these techniques are very usefulfor applications where manual image de-noising is acceptable II. METHODOLOGYthey fall short of their goals in many other applications that In principle, the noise identification method proposed herecall for automated image restoration. In response to these consists of three key steps:automated techniques, identification of image noise is of 28© 2011 ACEEEDOI: 01.IJNS.02.03.169
  2. 2. ACEEE Int. J. on Network Security , Vol. 02, No. 03, July 2011Step 1. Extract some representative noise samples from the To start with, we first assume that the type of noise isgiven noisy image, unknown, but it belongs to one of N known classes. For eachStep 2. Estimate some of their statistical features, and type of noise, we choose a simple linear or nonlinear spatialStep 3. Use a probabilistic neural network to identify the type filter operator capable of removing most of the noise of thisof noise. type from the image. Suppose Hk (i, j), 1d” k d” N, denoteThe architecture design for our proposed method is as shown these filter operators. To extract some noise samples, firstin figure 1. process the image through each filter operator to obtain: Where * denotes the associated filtering operation. Next, subtract each processed image, gk (i, j), 1d” k d” N, from f(i,j) to extract representative noise samples, wk(i,j), corresponding to each type of noise: Figure 1. Architecture Design of Proposed Method Next, estimate some simple statistical features fromIt consists of three key steps: filtering, feature extraction and and then classify the noise into one ofclassification. Filtering is done to get different noise samples N known classes using PNN.from the given input noisy image. The given input noisyimage is applied with three filters namely wiener filter, median III. IMPLEMENTATIONfilter and homomorphic filter to get three noise samples. In this paper, we consider four different types of commonlyFiltering is followed by feature extraction in which we are occurring image noise, namely, non gaussian, gaussian,extracting the features called kurtosis and skewness. Since speckle, and salt-and-pepper noise. Among these four types,we get three noise samples from the filtering step, totally we speckle noise is of multiplicative type, whereas the otherwill get three values of kurtosis and three values of skewness. three are additive in nature. The filters selected for the aboveThese six values are given as input to the probabilistic neural four types of noise are wiener filter for uniform or gaussiannetwork. The network is trained to identify the type of noise white noise, homomorphic filter for speckle noise, and medianthat affected the image. The algorithm for the proposed method filter for salt-and-pepper noise. Also, the statistical featurescan be given as below: studied here include “kurtosis” and “skewness”. Table I listsInput: Grayscale Noisy image the “kurtosis” and “skewness” values, and the selected filtersOutput: Statistical Parameters and Type of Noise for the four types of noise.1. Take the noisy image as an input.2. Generate the training data set sequences. TABLE I. KURTOSIS, SKEWNESS AND FILTERS SELECTED FOR FOUR3. Apply the three selected type of filters to the noisy image TYPES OF NOISEto get the estimates for the original image. yˆ wiener (i, j) = f (i, j) * Hwiener (i, j) yˆmedian (i, j) = f (i, j)* Hmedian (i, j) yˆhomo (i, j) = Exp [log (f (i, j)) * HWiener (i, j)]4. Get the three noise estimates based on the outputfrom the three filters. ωwiener = f (i, j) – yˆ wiener (i, j) ωmedian = f (i, j) – yˆ median (i, j) ωhomo = f (i, j) / yˆ homo (i, j)5. Calculate the statistical parameters such as kurtosis and From Table I, we can see that different type of noise haveskewness. different kurtosis or skewness values and those differences6. Identify the type of noise using neural network. can be used to identify the noise type. Kurtosis is a measureThe above steps can be summarized as follows. of how outlier-prone a distribution is. The kurtosis of theAssume the original MxN image y (i, j) is contaminated by normal distribution is 3. Distributions that are more outlier-some type of noise, ω (i, j).The observed image f(i, j) can be prone than the normal distribution have kurtosis greater thanmodeled by either equation (1) for additive noise, or equation 3; distributions that are less outlier-prone have kurtosis less(2) for multiplicative noise: than 3. Skewness is a measure of the asymmetry of the data f(i, j) = y (i, j) + ω (i, j), around the sample mean. If skewness is negative, the data 1<= i <= M, 1<= j <= N (1) are spread out more to the left of the mean than to the right. If skewness is positive, the data are spread out more to thef (i, j) = y (i, j) ω (i, j), right. The skewness of the normal distribution (or any perfectly1<= i <= M, 1<= j <= N (2) symmetric distribution) is zero. The kurtosis and skewness 29© 2011 ACEEEDOI: 01.IJNS.02.03.169
  3. 3. ACEEE Int. J. on Network Security , Vol. 02, No. 03, July 2011of a distribution is defined as that decides which of its input from the summation units is K= E(x-µ)4 /σ4 (5) the maximum, and finally output classified results. The typical S =E(x-µ)3 /σ3 (6) PNN architecture for the proposed method is shown belowWhere µ is the mean of x, σ is the standard deviation of x, and and consists of following:E(t) represents the expected value of the quantity t. • No of input nodes =6skewness computes a sample version of this population • No of nodes in pattern layer =8value. • No of nodes in summation layer = 4A. Extraction of Noise Samples from Input Noisy Image . • No of nodes in decision layer =1 First, the three selected filters, Wiener filter, homomorphicfilter, and median filter, are applied to the noisy image f (i, j) toget three different estimates yˆ(i,j) for the original image y (i,j) as follows: yˆ wiener (i, j) = f (i, j) * Hwiener (i, j) (7) yˆmedian (i, j) = f (i, j)* Hmedian (i, j) (8) Figure 2. PNN architecture for proposed method yˆhomo (i, j) = Exp [log (f (i, j)) * HWiener (i, j)] (9) The three filters wiener filter, median filter and Where, the symbol * above denotes the associated spatial homomorphic filter are applied to the given noisy input imagefiltering operations. Then we get three noise estimates based to get three values of kurtosis and three values of skewness.on the outputs from the three filters as follows: These six features are given as input to the neural network ωwiener = f (i, j) – yˆ wiener (i, j) (10) and then the network is trained to identify the type of noise ωmedian = f (i, j) – yˆ median (i, j) (11) that affected the image. The output layer decides which of its ωhomo = f (i, j) / yˆ homo (i, j) (12) input from the summation units is the maximum, and finally Next, the noise estimates obtained in equations (10)-(12) are outputs classified results.used to identify the noise type. IV. EXPERIMENTAL RESULTSB. Estimation of Some of Statistical Features. All experiments were carried out using Matlab. Matlab In our proposed method, we are extracting two features function “imnoise” is used to generate Gaussian white noise,namely Kurtosis and Skewness. These values are used to speckle noise and salt-and-pepper noise. We have conductedevaluate how close ωWiener is to Gaussian or uniform white simulations on different image sequences. Figures (3a)-(3d)noise, ω Median is to salt-and-pepper noise, and ω Homo is to represent one of the noisy image sequences as an example.speckle noise. To measure these similarities, we need some With four different noise inputs, the calculated kurtosis,expected reference values to compare with. The expected skewness and identified noise types are shown in Table II.reference values can be obtained by filtering the appropriatenoise sequences and evaluating the Kurtosis and Skewnessof the filter outputs. For instance, the procedure to obtainthe expected reference values corresponding to salt-and-pepper noise, we do the following:First generate training sequences of Salt- and-Pepper noise.Filter each noise sequence through the medianfilterThen estimate the Kurtosis and Skewness of each filterednoise sequence and compute their average to yield thereference values of Kurtosis and Skewness for the salt-and-pepper noise.C. PNN for Classification Figures (3a)-(3d): Clockwise from left (Gaussian white noise, A typical structure of PNNs is organized into a speckle noise, salt and pepper noise, non- Gaussian white noise)multilayered feed forward network with four layers: Input We also conducted testing for the same training set tolayer, pattern layer, summation layer and, Output layer. The different images by adding different percentage of noise forinput layer accepts input vectors. The non-linear dot product all the four types. The obtained results are shown in table IIIprocessing of input vectors and weight vectors is and the result of %of noise vs. accuracy is plotted as shownimplemented in the pattern layer. The pattern layer is the core in figure 4. From figure 4, we can notice that the percentageof a PNN. During training, the pattern vectors in the training of accuracy for classifying different types of noise is efficientset are simply copied to the pattern layer of the PNN. The for less percentage of noise. But since the values of kurtosisclassified samples probabilities are calculated in the and skewness will vary by adding more noise, the performancesummation layer. The output layer is a threshold discriminator at higher rate of noise decreases. 30© 2011 ACEEEDOI: 01.IJNS.02.03. 169
  4. 4. ACEEE Int. J. on Network Security , Vol. 02, No. 03, July 2011 TABLE II. AN EXAMPLE OF KURTOSIS, SKEWNESS AND IDENTIFIED V. CONCLUSION NOISE TYPES A neural network based technique for identifying the type of noise present in a noisy image is proposed in this paper. The proposed method exhibits fast training process and does not require any assumption in the given images such as homogeneous areas etc. The proposed technique can be used with a variety of de-noising filters. The results of simulation studies seem to indicate that the method is capable of accurately determining the type of noise. Figure 4. % of Noise vs. Accuracy TABLE III. ACCURACY OF FOUR TYPES OF NOISE [5] L. Beaurepaire, K. Chehdi, and B. Vozel, “Identification of the REFERENCES nature of noise and estimation of its statistical parameters by[1] Yixin Chen, Manohar Das, “An Automated Technique for Image analysis of local histograms,” In Proceedings of ICASSP’97, AprilNoise Identification Using a Simple Pattern Classification 21-24, Munich, 1997Approach,” pp. 819-822, 2007 IEEE. [6] D.Zhang, Z.Wang, “Impulse Noise detection and Removal[2] A. M. Tekalp, H. Kaufman, J. W. Woods, “Edge-adaptive Using Fuzzy Techniques”, 27th February, 1997, vol.33, No.5.Kalman filtering for image restoration with ringing suppression,” [7] Alain Bretto, Hocine Cherifi, “Noise Detection and Cleaning byIEEE Transactions on Acoustics, Speech, and Signal Processing, Hypergraph Model”, Computer Vision Graphics and ImageJune 1989, Vol. 37, pp. 892-899.[3] L. Gagnon, A. Jouan, “Speckle Filtering of SAR Images: A Processing, 265–277, September 1997Comparative Study between Complex Wavelet-Based and Standard [8] Xiaosheng LIU, Zhihui Chen, “Research on Noise DetectionFilters,” Proc. SPIE, 1997, Vol. 3169, pp. 80-91. Based on Improved Gradient Function”, International Symposium[4] T. Chen, K. K. Ma, L. H. Chen, “Tri-state median filter for on Computer Science and Computational technology, 2008image denoising,” IEEE transactions on image processing (IEEE [9] Prashant Kumar Patra, Manojranjan Nayak, Simant Kumartransactions on image processing, 1999, Vol. 8, No. 12, pp. 1834- Nayak, Nataraj Kumar Gobbak, “Probabilistic Neural Network1838. for Pattern Classification,” 2002 IEEE [10] Gonzalez, Woods, and Eddins, “Digital image Processing Using MATLAB”, 2002 Edition. 31© 2011 ACEEEDOI: 01.IJNS.02.03.169

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