Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms, such as filtering approach, wavelet based approach, and multifractal approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Brownian noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise.

Image Processing, Denoising, Pattern Recognition and Image Enhancement

[1] D. L. Donoho and I. M. Johnstone, �Adapting to unknown smoothness via wavelet shrinkage,� J. Amer. Statist. Assoc., vol. 90, pp. 1200-1224, 1995.
[2] M. K. Mihcak, I. Kozintsev, K. Ramchandran, and P. Moulin, �Low-complexity image denoising based on statistical modeling of wavelet coefficients,� IEEE Signal Process. Lett., vol. 6, pp. 300-303, 1999.
[3] S. G. Chang, B. Yu, and M. Vetterli, �Spatially adaptive wavelet thresholding with context modeling for image denoising,� IEEE Trans. Image Process., vol. 9, pp. 1522-1531, 2000.
[4] I. Prudyus, S. Voloshynovskiy, and A. Synyavskyy, �Wavelet-based MAP image denoising using probably better class of stochastic i.i.d. image models,� Proc. Int. Conf. Telecommun., Modern Satellite, Cable and Broadcasting Service, pp. 583-586, 2001.
[5] L. Kaur, S. Gupta, and R. C. Chauhan, �Image denoising using wavelet thresholding,� Proc. Int. Conf. Computer Vision, Graphics and Image Process., pp. 1-4, 2002.
[6] L. Sendur and I. W. Selesnick, �Bivariate shrinkage with local variance estimation,� IEEE Signal Process. Lett., vol. 9, pp. 438-441, 2002.
[7] A. Achim and E. E. Kuruoglu, �Image denoising using bivariate α-stable distributions in the complex wavelet domain,� IEEE Signal Process. Lett., vol. 12, pp. 17-20, 2005.
[8] A. Pizurica and W. Philips, �Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising,� IEEE Trans. Image Process., vol. 15, pp. 654-665, 2006.
[9] F. Luisier, T. Blu, and M. Unser, �A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding,� IEEE Trans. Image Process., vol. 16, pp. 593-606, 2007.
[10] H. Rabbani, �Image denoising in steerable pyramid domain based on a local Laplace prior,� Pattern Recognition, vol. 42, pp. 2181-2193, 2009.
[11] Q. Guo and S. Yu, �Image denoising using a multivariate shrinkage function in the curvelet domain,� IEICE Electron. Express, vol. 7, pp. 126-131, 2010