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Applicability of BPN and MLP Neural Networks for Classification of Noises Present in Different Image Formats
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 26 - Number 5 |
| Year of Publication: 2011 |
| Authors: T. Santhanam, S. Radhika |
10.5120/3101-4259
|
T. Santhanam, S. Radhika . Applicability of BPN and MLP Neural Networks for Classification of Noises Present in Different Image Formats. International Journal of Computer Applications. 26, 5 ( July 2011), 10-14. DOI=10.5120/3101-4259
Abstract
Images exist in different formats in real time applications. There is no prescribed format in which an image should be presented as input to any image processing algorithm. This article experiments a neural network approach to classify the noises present in an image given in BMP (Bitmap), JPG/JPEG(Joint Photographic Experts Group), TIF/TIFF(Tagged Image File Format), GIF(Graphics Interchange Format) and PNG(Portable Network Graphics) format. The noises in the image are classified by extracting the statistical features like skewness and kurtosis, which is then applied to the Back Propagation Network (BPN) and Multi Layer Perceptron (MLP). This is done for images of all the formats. MLP is superior in classifying salt and pepper noise in images stored in PNG format. BPN is performing well in classifying Gaussian white noise in images stored in BMP format. The study throws light on the type of neural network to be employed for classifying the different noises present in images of different formats, which will prove to be useful in enhancing the image for further processing.
References
- Image Denoising available at http://www.codeding.com/?article=10
- Luo, W., 2006. Efficient Removal of Impulse Noise from Digital Images. IEEE Transaction on Consumer Electronics, vol. 52, pp. 523-527
- Abreu, E., M. Lightstone, S.K. Mitra and K. Arakawa, 1996. A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Trans. Image Process., 5: 1012-1025
- Hammond, D.K. and E.P. Simoncelli, 2006. Image denoising with an orientation-adaptive gaussian scale mixture model. Proceedings of the IEEE International Conference on Image Processing, Oct. 8-11, Atlanta, GA., pp: 1433-1436
- Russo, F., 2000. Noise removal from image data using recursive neurofuzzy filters. IEEE Trans. Instrument. Measure., 49: 307-314
- Motwani, M.C. and F.C. Harris, 2004. Survey of image denoising techniques. Proceedings of the GSPx, Sept. 27-30, Santa Clara, CA., pp: 27-30.
- Chehdi, K. and M. Sabri, 1992. A new approach to identify the nature of the noise affecting an image. Proc. IEEE Int. Conf. Acoustics, Speech Signal Process., 3: 285-288.
- Beaurepaire, L., K. Chehdi and B. Vozel, 1997. Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms. Proc. IEEE Int. Conf. Acoustics, Speech Signal Process., 4: 2805-2808.
- Vozel, B., K. Chehdi, L. Klaine, V.V. Lukin and S.K. Abramov, 2006. Noise identification and estimation of its statistical parameters by using unsupervised variational classification. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, May 14-19, IEEE Xplore Press, Toulouse, pp: 841-844.
- Chen, Y. and M. Das, 2007. An automated technique for image noise identification using a simple pattern classification approach. Proceedings of the MWSCAS, (MWSCAS'07), IEEE Computer Society, USA., pp: 819-822.
- Santhanam, T. and S. Radhika, 2010. A novel approach to classify noises in images using artificial neural network. Journal of Computer Science 6 (5): pp. 506 -510.
- Senol, D. and M. Ozturan, 2008. Stock price direction prediction using artificial neural network approach: The case ofTurkey.J.Artif.Intell.,1:70-77.
- Eriki, P.O. and R.I. Udegbunam, 2008. Application of neural network in evaluating prices of housing units in Nigeria:Apreliminaryinvestigation.J.Artif.Intell.,1:21-27.
- Werbos, P., 1974. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. Thesis, Harvard University, Cambridge, Mass.
- Rumelhart, D. E., J.L. McClelland and the PDP Research Group, 1986. Parallel Distributed Processing Exploration in the Micro Structure of Cognition.. Vol. 1,. MIT Press, Cambridge, MA, pp: 547.
- Parker, D., 1982. Learning logic. Invention Report S81-64, File 1, Office of Technology Licensing, Stanford University.
- Kumaravel, N. and T.K. Reddy, 2009. Texture Analysis of Bone CT Images for Classification and Characterization of Bone Quality”, International Journal of Soft Computing 4(5), pp. 223-228.
- Ibrahim, Z., D. Isa, R. Rajkumar and G. Kendall, 2009. Document zone content classification for technical document images using artificial neural networks and support vector machines. Proceedings of the 2nd International Conference on the Applications of Digital Information and Web Technologies, Aug. 4-6, London, pp: 345-350.
- Khanale, P.B. and S.D. Chitnis, 2011. Handwritten Devanagiri Character Recognition using Artificial Neural Network. Journal of Artificial Intelligence 4(1) pp 55-62.
- Coban, H., 2004. Application of an Artificial Neural Network (ANN) for the identification of grapevine (Vitis viniferaL.)genotypes.AsianJ.PlantSci.,3:340-343.
- Md Saad, M.H., M.J. Mohd Nor, F.R.A. Bustami and R. Ngadiran, 2007. Classification of heart abnormalities using artificialneuralnetwork.J.AppliedSci.,7:820-825.
- Foody, G.M., 2001. Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches. J. Geogr. Syst., 3: 217-232.
- Antani, A., L.R. Long, G.R. Thoma and R.J. Stanley, 2003. Vertebra shape classification using MLP for content-based image retrieval. Proc. Int. Joint Conf. Neural Networks, 1: 160 – 165.
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