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Revision with unchanged content. Image denoising is a process to restore a noisy image so that the other system (or human) can understand it better. Since denoising is a challenging ill-posed problem, it has attracted many researchers. In this book, we discuss some efficient approaches for image denoising using wavelet transforms. For the last decade, wavelet-based approaches have been studied widely due to its superb performance and nice properties such as multiresolution and energy compaction. The book contains mainly following contents: First, some important wavelet transforms for image denoising are described. Second, a review on various denoising algorithms will help readers understand the trend. Some efficient algorithms take account into spatial and scale dependency in the wavelet domain or statistical modeling with Bayesian approach. The experimental results on various wavelet-based algorithms are also analyzed. Wavelet-based methods produce comparatively higher signal to noise ratio and less visual artifact than other methods. The book is addressed to students, graduates, professionals, and researchers who are interested in image processing.

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The author received B.Sc. and M.S. in computer science respectively from Sogang University, Seoul and Concordia University, Montreal. He is currently a senior computer vision specialist at Genetec Inc. , Montreal while pursuing Ph.D. in the same field. His research interests include image/video processing, pattern recognition, andcomputer vision.

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