International Journal of Electronics and JOURNALEngineering & Technology (IJECET), ISSN 0976 –
INTERNATIONAL Communication...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 09...
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40120130406002

  1. 1. International Journal of Electronics and JOURNALEngineering & Technology (IJECET), ISSN 0976 – INTERNATIONAL Communication OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December, 2013, pp. 14-20 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET ©IAEME EFFICIENT ALGORITHM BASED ON BLIND SOURCE SEPARATION INDEPENDENT COMPONENT ANALYSIS USING MATLAB Nishant Tripathi1 and Dr. Anil Kumar Sharma2 M. Tech. Scholar1, Professor & Principal2, Department of Electronics & Comm. Engg. Institute of Engineering & Technology, Alwar-301030 (Raj.), India ABSTRACT Independent component analysis is a lively field of research and is being utilized for its potential in statistically independent separation of images. ICA based algorithms has been used to extract interference and mixed images and a very rapid developed statistical method during last few years. So, in this paper an efficient result oriented algorithm for ICA-based blind source separation has been presented. In blind source separation primary goal is to recover all original images using the observed mixtures only. Independent Component Analysis (ICA) is based on higher order statistics aiming at penetrating for the components in the mixed signals that are statistically as independent from each other as achievable. Keywords: ICA, Mixer Signal, Blind Source Separation, PSNR. 1. INTRODUCTION Blind source separation is a technique that extracts the original signals from the mixture. Such kind of technique find their unique application in the area of image processing, satellite navigation of different object locating, mixed object image separation and several more biomedical and neural networking areas. BSS-ICA can find a potential application in separating individual images from a mixed noisy picture with knowing the actual proportion of their mixtures. The ICA algorithm is based on the iteration to come across for the greatest of the non-gaussianity of variables. This paper is structured in the subsequent sections: Initially a brief introduction of ICA is provided in section I. Later on in section II, the basic algorithm is introduced which performs ICA by maximize the non Gaussianity of the signal components. In Section III, the simulation results for noisy mixed images separation using the algorithm have been shown. 14
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME 2. PRINCIPLES OF ICA ICA is a method of performing blind signal separation that aims to pick up unknown sources from a set of the experimental values, in which they are mixed in an unfamiliar manner. In fundamental ICA model, the pragmatic mixture signals x (t) can be expressed as; x (t) = As(t) (1) where A is an unknown mixing matrix, and s (t) represents the latent source signals which is supposed to be statistically equally independent. The ICA model describes an additive noise vector n (t) , and gives a more sensible, broad ICA model in the noising case: x (t) = As (t) + n (t) (2) The independent components s(t) cannot be directly observed and the mixing coefficients A and the noise n (t) is also assumed to be unknown. If noise is negligible, only the random variables x(t) is observed and both the components s(t) and the coefficients A must be estimated using x(t) .Then, the ICA solution is obtained in an unsupervised way that finds a de-mixing matrix C. The de mixing matrix W is used to transform the observed mixture signals x (t) to give the independent signals. That is: sˆ (t) = Cx(t) (3) The signals sˆ (t) are the close estimation of the latent source signals s (t). If C = A-1, then the recovered signals sˆ (t) are exactly the original sources s(t) . The components of sˆ (t), called independent components, are required to be as mutually independent as possible. Some main functions in ICA are; • • • Non-Gaussian Nature: The ICA is non-Gaussian in nature. In fact, without such a property the close assessment is not possible at all. In most of conventional statistical theory, random variables are assumed to have Gaussian distributions, so they don’t include and justify the ICA. Measures of non-Gaussianity: The non-Gaussian nature in ICA is measured effectively through quantities analysis of the non Gaussianity of signal variables. Kurtosis:The classical measure of non-Gaussianity is kurtosis or the fourth-order cumulate. The kurtosis of y is classically defined by Kurt(y) = E{x4} – 3(E{x2}) 2 (4) Negentropy: Negentropy is also one of the most effective measures for non-Gaussian nature of random variable. It is measured through information theoretic content in the variable. 3. PROPOSED ALGORTHEM The proposed algorithm follows as: • Pre-processing for ICA: It is quite essential and helpful to work on some pre-processing on the available data. In this section, we talk about some pre-processing techniques that make the difficulty of ICA estimation simpler and improved accustomed. • Centering: It is generally the essential and compulsory pre-processing, which is used to 15
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME • • • center the” x”, i.e. subtract its mean vector m. E{x} so as to make x a zero-mean variable. This implies that input signal is zero-mean as well, as can be seen by taking expectations on both sides of vector–matrix notation. Whitening: It is an additional helpful pre-processing technique in ICA which is used to first whiten the observed variables. This means that before the application of the ICA algorithm (and after centering), we transform the observed vector x linearly so that we obtain a new vector x which is white, i.e. its components are uncorrelated and their variances is equal to unity. Further preprocessing: The accomplishment of ICA for a specified data set may depend very much on performing some application-dependent pre-processing steps. For example, if the data consists of time-signals, some band-pass filtering may be very useful. ICA Algorithm: Using the fixed point algorithm with the contrast function and fixing the norm of the weights to one, the algorithm for the already sphered data, is: w+ = E{xg(wTx)}- E{g’(wTx)}c (5) w = w+/ w (6) + ; Where w* is a column vector with the estimation of one line of the matrix W. The algorithm can be directly applied to themixed data X (usually not sphere): W+=C-1E{Xg(WTX)}–E{g’(WTX)}W (7) W*=W+/W+T CW+ (8) Where c = E {XXT} is the covariance matrix of the mixed data. This algorithm gives one weight vector and thus only one independent component. The other components can be obtained using deflation. Fig. 1 shows the flowchart for ICA based Algorithm. This is the shortest iteration to achieve non-guassianity Fig. 1 Flowchart for the ICA based algorithm 16
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME The basic steps for algorithms are as follows: (i) Initialize the weight vector w (with random values); (ii) Let w+ = E {xg (wT x)} - E {g’ (wT x)} w …, where g is the derivative of G (iii) Let w = w+ / ||w+ ||; (iv) If not converged, return to step 2. This process is known as a one-unit learning algorithm. The above algorithm is only designed to estimate one of the independent components to compute a matrix W without giving first choice to any fussy independent component, the next algorithm may be added to the end of each iteration of the above algorithm. Thus we obtain the algorithm as follows: (1) Center the data to make its mean zero; (2) Whiten the data to get xˆ(t) ; (3) Make i=1; (4) Choose an initial orthogonal matrix for W and make k=1; (5) Make wi (k) = [ (wi(k-1)T)3] - 3wi(k-1) (6) Make wi (k) = wi(k)/ wi(k) (7) If not converged, make k=k+1 and go back to step (5) (8) Make i=i+1. (9) When i<number of original signals, go back tostep (4). 4. RESULT AND ANALYSIS We simulate the system with independent sources of different kurtosis values. These basesstood normalized to have aentity variance and were mixed according to signal model. In simulation result signals are producedarbitrarily and mixing process in MATLAB has been done. The separated signal is recuperated using ICA algorithm. We have carefully chosen several different merging combination of 5 different images to provide different sample of proportionate mixture of mixed images and then has calculated the PSNR of original and ICA separated individual images before mixing and and after de-mixing respectively. Thus algorithm performance analysis has been described in figures (2-6) and comparison result related to PSNR is described in table-1.The peaksignal-to-noise ratio (PSNR), between the original image X and the mixed or separated image Y, is calculated using Eqn. (9) and (10). PSNR = 10 log ((M*N)2)/MSE MSE = 1/MN ΣMi=1ΣNi=1 (Y(i,j) – X(i,j))2 17 (9) (10)
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME Fig. 2: Sample Numer.1 Fig. 3: Sample Numer.2 Fig. 4: Sample Numer.3 Fig. 5: Sample Numer.4 Fig. 6: Sample Numer.5 18
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME TABLE.1: COMPARISON BETWEEN ORIGINAL IMAGE PSNR AND ICA EXTRACTED PSNR Sample Image Original Recovered S. No No. Name PSNR(Db) PSNR(Db) Bank 27.24 28.25 Cam1 25.93 21.22 1 Fig.1 Cam2 27.22 19.11 Bank 28.12 27.99 Cam1 27.62 25.45 2 Fig.2 Lena 25.32 25.88 Monk 27.97 22.34 Cam1 25.26 23.11 3 Fig.3 Bank 27.26 26.22 Cam2 27.41 26.2 Lena 28.24 27.11 4 Fig.4 Bank 27.69 26.33 Cam1 28.111 22.01 5 Fig.5 Monk 26.67 23.101 Lena 27.11 24.022 5. CONCLUSION This paper proposes a short iterative efficient algorithm based blind source separation using Independent component Analysis in MATLAB environment. This method is proposed to have independent component analysis to separate mixed images. The simulation results showed that the algorithm can blindly separate the original images from the mixed images with good accuracy. However, this paper just does some elementary research under the basic noise-free ICA model. In practice, we need to process many contaminated images, as they contain much unknown noise, problem like this make it necessary for us to extend the basic framework of ICA during the process of future research. REFERENCES [1] [2] [3] [4] [5] M.H. Sadeghi, M.R Aghabozorgi and M.T. Sadeghi, “Removing Reflection from Image Using ICA”, International Symposium on Telecommunications, 2008. IST 2008, PP. 815 – 820, IEEE. Hong-yan Li, Qing-hua Zhao, Jing-qing Zhao, Bao-jin Xiao, “Blind Separation of Noisy Mixed Images Based on Wiener Filtering and Independent Component Analysis”, 2nd International Congress on Image and Signal Processing, 2009. CISP '09, PP. 1 – 5, IEEE. Chao Ma, Lian-min Wang, “Review of ICA Based Fixed-Point Algorithm for Blind Separation of Mixed Images”, 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 2010, PP.1 – 3, IEEE. F.Asano, S.Ikeda, M.Ogawa, Combined approach of array processing and independent component analysis for blind separation of acoustic signals.IEEE Transactions on Speech and Audio Processing,2003,11 (3), PP.204~215. P.Rajkishore, S.Hiroshi., S.Kiyohiro, Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation, IEICE Trans Fundamentals, 2005, PP. 1683-1692. 19
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November-December (2013), © IAEME [6] H.Saruwatari, T.Kawamura, K.Shikano, Fast-convergence algorithm for ICA-based blind source separation using array signals processing, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 2001, PP.464-467. [7] Huang Qihong, Wang Shuai, Liu zhao, Improved image feature extraction based on independentcomponent analysis, OptoElectronic, 2007, 34(1), PP.123-125. [8] K. Usman, H. Juzoji, I. Nakajima and M.A. Sadiq, “A study of increasing the speed of the independent component analysis (ICA) using wavelet technique” in Proc. International Workshop on Enterprise Networking and Computing in Healthcare Industry (HEALTHCOM 2004). pp. 73 – 75, 28-29 June 2004. [9] R. Moussaoui, J. Rouat and R. Lefebvre, "Wavelet Based Independent Component Analysis for Multi-Channel Source Separation" in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing,(ICASSP 2006) vol. 5, pp V645-V648, 14-19 May 2006. [10] N. Hirai, H. Matsumoto, T. Furukawa and K. Furuya, “A consideration of blind source separation using wavelet transform” in Proc. IEEE International Conference on Circuits and Systems (ISCAS 2005) vol. 6, pp 5722 - 5725, 23-26 May 2005. [11] Chandrika V, Parvathi C.S. and P. Bhaskar, “Design and Development of Pulmonary Tuberculosis Diagnosing System using Image Processing Techniques and Artificial Neural Network in Matlab”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 357 - 372, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. 20

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