Abstract
Images corrupted by noise requires enhancement for subsequent processing. Traditional approaches of denoising rely upon spatial, statistical, and spectral properties of image which at times fails to capture the finite details. Discrete wavelet transform (DWT) is a commonly adopted method for image processing applications. Fuzzy-based systems are suitable for modeling uncertainty. In the proposed work, we present a hybrid approach which combines multilevel DWT and adaptive neuro-fuzzy inference system (ANFIS) to capture the benefits of two different domains into a single framework. We apply our algorithm to denoise the images corrupted by multiplicative noise like speckle noise. The results obtained shows that the proposed method proves effective for denoising of images.
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References
Sbou, Y.A.Al.: Artificial neural networks evaluation as an image denoising tool. World Appl. Sci. J. 17(2), 218–227 (2012)
Krishnan, M.H., Viswanathan, R.: A new concept of reduction of Gaussian noise in images based on fuzzy logic. Appl. Math. Sci. 7(12), 595–602 (2013)
Sau, K., Chanda, A., Karmakar, P.: An unique edge preserving noise filtering technique for impusive noise removal. Int. J. Signal Image Process. 2(3), 33–46 (2011)
Kaur, M., Mandal, D.: Speckle noise reduction in medical ultrasound images using particle swarm optimization with artificial neural networks. Int. J. Electron. Commun. Technol. 2(3), 147–150 (2011)
Ragesh, N.K., Anil, A.R., Rajesh, R.: Digital image denoising in medical ultrasound images: a survey. Int. J. Artif. Intell. Mach. Learn. 67–73 (2011)
Vishwa, A., Sharma, S.: Speckle noise reduction in ultrasound images by wavelet thresholding. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(2) (2012)
Ufade, A.S., Khadse, B.K., Suralkar, S.R.: Restoration of blur image using wavelet based image fusion. Int. J. Eng. Adv. Tech. 2(2), 159–161 (2012)
Ruikar, S.D., Doye, D.D.: Wavelet based image denoising technique. Int. J. Adv. Comput. Sci. Appl. 2(3), 49–53 (2011)
Jang, J.S.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Sys. Man Cybern. 23(3), 665–685 (1993)
Kanika, D.N., Sharma, K.: Comparative analysis of discrete wavelet transform and fast wavelet transform on image compression. Int. J. Eng. Res. Tech. 1(5), 1–7 (2012)
Saikia, T., Sarma, K.K.: Multilevel-DWT based image de-noising using feed forward artificial neural network. In: IEEE International Conference on Signal Processing and Integrated Networks, pp. 791–794 (2014)
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Saikia, T., Sarma, K.K. (2015). Multilevel-DWT-Based Image Denoising Using Adaptive Neuro-Fuzzy Inference System. In: Bora, P., Prasanna, S., Sarma, K., Saikia, N. (eds) Advances in Communication and Computing. Lecture Notes in Electrical Engineering, vol 347. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2464-8_7
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DOI: https://doi.org/10.1007/978-81-322-2464-8_7
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