Efficient Algorithms for Image Denoising using Wavelets

Abstract

The fundamental ideas of this thesis are based on the observation that there are high scopes of enhancing the efficiency in Image Denoising Algorithms (IDAs). Noises in the digital images are induced during their acquisition and transmission due to the imperfect nature of digital instruments. For instance, Additive White Gaussian Noise (AWGN) is cased by poor quality image acquisition equipment and also inherited in communication channels. Various IDAs have been proposed to reduce the noise from images in spatial as well as transform domains. The IDAs in spatial domain can be further subdivided according to linear and non-linear approach. The transform domain IDAs are based on the choice of basis functions. After reviewing standard IDAs in spatial and transform domain, this thesis embarks on the endeavor of developing and experimenting new IDAs in wavelet domain that perform not only noise reduction but also preservation of image fine details and color components. Wavelet transform achieved more popularity in transform domain due to sparsity and multiresolution property. Discrete Wavelet Transform (DWT) of a noisy image can generate very sparse wavelet coefficients. Wavelet coefficient thresholding is achieved by calculating the threshold value adaptively. This can avoid the over-smoothening of noisy images and their fine details especially edges. DWT, Undecimated DWT (UDWT) and Dual-Tree Complex Wavelet Transform (DT-CWT) are the efficient wavelet transform functions for IDAs and used in this work. It is necessary to reduce the noises for further image processing while preserving the edges present in the image. An edge preserving adaptive algorithm for gray and color image denoising is proposed. The noisy images are decomposed using DWT to obtain their coefficients. The edges of an image are detected using the Canny edge detector in all the details subbands. Then two thresholds are iv calculated by using the Bayesian estimator. The adaptive standard threshold is used for flatten region and its updated version is