【正文】 he statistical properties of the wavelet coefficient seemed to outperform the thresholding techniques and gained ground. Recently, much effort has been devoted to Bayesian denoising in Wavelet domain. Hidden Markov Models and Gaussian Scale Mixtures have also bee popular and more research continues to be published. Tree Structures ordering the wavelet coefficients based on their magnitude, scale and spatial location have been researched. Data adaptive transforms such as Independent Component Analysis (ICA) have been explored for sparse shrinkage. The trend continues to focus on using different statistical models to model the statistical properties of the wavelet coefficients and its neighbors. Future trend will be towards finding more accurate probabilistic models for the distribution of nonorthogonal wavelet coefficients. 3. Classification of Denoising Algorithms As shown in Figure 1, there are two basic approaches to image denoising, spatial filtering methods and transform domain filtering methods. Spatial Filtering A traditional way to remove noise from image data is to employ spatial filters. Spatial filters can be further classified into nonlinear and linear filters. I. NonLinear Filters With nonlinear filters, the noise is removed without any attempts to explicitly identify it. Spatial filters employ a low pass filtering on groups of pixels with the assumption that the noise occupies the higher region of frequency spectrum. Generally spatial filters remove noise to a reasonable extent but at the cost of blurring images which in turn makes the edges in pictures invisible. In recent years, a variety of nonlinear median type filters such as weighted median, rank conditioned rank selection, and relaxed median have been developed to overe this drawback. II. Linear Filters A mean filter is the optimal linear filter for Gaussian noise in the sense of mean square error. Linear filters too tend to blur sharp edges, destroy lines and other fine image details, and perform poorly in the presence of signaldependent noise. The wiener filtering method requires the information about the spectra of the noise and the original signal and it works well only if the underlying signal is smooth. Wiener method implements spatial smoothing and its model plexity control correspond to choosing the window size. To overe the weakness of the Wiener filtering, Donoho and Johnstone proposed the wavelet based denoising scheme in. Transform Domain Filtering The transform domain filtering methods can be subdivided according to the choice of the basis functions. The basis functions can be further classified as data adaptive and nonadaptive. Nonadaptive transforms are discussed first since they are more popular. SpatialFrequency Filtering Spatialfrequency filtering refers use of low pass filters using Fast Fourier Transform (FFT). In frequency smoothing methods the removal of the noise is achieved by designing a frequency domain filter and adapting a cutoff frequency when the noise ponents are decorrelated from the useful signal in the frequency domain. These methods are time consuming and depend on the cutoff frequency and the filter function behavior. Furthermore, they may produce artificial frequencies in the processed image. Wavelet domain Filtering operations in the wavelet domain can be subdivided into linear and nonlinear methods. I. Linear Filters Linear filters such as Wiener filter in the wavelet domain yield optimal results when the signal corruption can be modeled as a Gaussian process and the accuracy criterion is the mean square error (MSE). However, designing a filter based on this assumption frequently results in a filtered image that is more visually displeasing than the original noisy signal, even though the filtering operation successfully reduces the MSE. In a waveletdomain spatiallyadaptive FIR Wiener filtering for image denoising is proposed where wiener filtering is performed only within each scale and intra scale filtering is not allowed. II. NonLinear Threshold Filtering The most investigated domain in denoising using Wavelet Transform is the nonlinear coefficient thresholding based methods. The procedure exploits sparsity property of the wavelet transform and the fact that the Wavelet Transform maps white noise in the signal domain to white noise in the transform domain. Thus, while signal energy bees more concentrated into fewer coefficients in the transform domain, noise energy does not. It is this important principle that enables the separation of signal from noise. The procedure in which small coefficients are removed while others are left untouched is called Hard Thresholding. But the method generates spurious blips, better known as artifacts, in the images as a result of unsuccessful attempts of removing moderately large noise coefficients. To overe the demerits of hard thresholding, wavelet transform using soft thresholding was also introduced in. In this scheme, coefficients above the threshold are shrunk by the absolute value of the threshold itself. Similar to soft thresholding, other techniques of applying thresholds are semisoft thresholding and Garrote thresholding. Most of the wavelet shrinkag