外文翻译---图像去噪技术研究

外文翻译---图像去噪技术研究内容摘要：

guish noise from the signal solely based on coefficient magnitudes is violated when noise levels are higher than signal magnitudes. Under this high noise circumstance, the spatial configuration of neighboring wavelet coefficients can play an important role in noisesignal classifications. Signals tend to form meaningful features (. straight lines, curves), while noisy coefficients often scatter randomly. III. Nonorthogonal Wavelet Transforms Undecimated Wavelet Transform (UDWT) has also been used for deposing the signal to provide visually better solution. Since UDWT is shift invariant it avoids visual artifacts such as pseudoGibbs phenomenon. Though the improvement in results is much higher, use of UDWT adds a large overhead of putations thus making it less feasible. In normal hard/soft thresholding was extended to Shift Invariant Discrete Wavelet Transform. In Shift Invariant Wavelet Packet Deposition (SIWPD) is exploited to obtain number of basis functions. Then using Minimum Description Length principle the Best Basis Function was found out which yielded smallest code length required for description of the given data. Then, thresholding was applied to denoise the data. In addition to UDWT, use of Multiwavelets is explored which further enhances the performance but further increases the putation plexity. The Multiwavelets are obtained by applying more than one mother function (scaling function) to given dataset. Multiwavelets possess properties such as short support, symmetry, and the most importantly higher order of vanishing moments. This bination of shift invariance amp。 Multiwavelets is implemented in which give superior results for the Lena image in context of MSE. IV. Wavelet Coefficient Model This approach focuses on exploiting the multiresolution properties of Wavelet Transform. This technique identifies close correlation of signal at different resolutions by observing the signal across multiple resolutions. This method produces excellent output but is putationally much more plex and expensive. The modeling of the wavelet coefficients can either be deterministic or statistical. a. Deterministic The Deterministic method of modeling involves creating tree structure of wavelet coefficients with every level in the tree representing each scale of transformation and nodes representing the wavelet coefficients. This approach is adopted in. The optimal tree approximation displays a hierarchical interpretation of wavelet deposition. Wavelet coefficients of singularities have large wavelet coefficients that persist along the branches of tree. Thus if a wavelet coefficient has strong presence at particular node then in case of it being signal, its presence should be more pronounced at its parent nodes. If it is noisy coefficient, for instance spurious blip, then such consistent presence will be missing. Luetal. [24], tracked wavelet local maxima in scalespace, by using a tree structure. Other denoising method based on wavelet coefficient trees is proposed Donoho. b. Statistical Modeling of Wavelet Coefficients This approach focuses on some more interesting and appealing properties of the Wavelet Transform such as multiscale correlation between the wavelet coefficients, local correlation between neighborhood coefficients etc. This approach has an inherent goal of perfecting the exact modeling of image data with use of Wavelet Transform. A good review of statistical properties of wavelet coefficients can be found in and . The following two techniques exploit the statistical properties of the wavelet coefficients based on a probabilistic model. i. Marginal Probabilistic Model A number of researchers have developed homogeneous local probability models for images in the wavelet domain. Specifically, the marginal distributions of wavelet coefficients are highly kurtosis, and usually have a marked peak at zero and heavy tails. The Gaussian mixture model (GMM) and the generalized Gaussian distribution (GGD) are monly used to model the wavelet coefficients distribution. Although GGD is more accurate, GMM is simpler to use. In, authors proposed a methodology in which the wavelet coefficients are assumed to be conditionally independent zeromean Gaussian random variables, with variances modeled as identically distributed, highly correlated random variables. An approximate Maximum A Posteriori (MAP) Probability rule is used to estimate marginal prior distribution of wavelet coefficient variances. All these methods mentioned above require a noise estimate, which may be difficult to obtain in practical applications. Simoncell and Adelson used a twoparameter generalized Laplacian distribution for the wavelet coefficients of the image, which is estimated from the noisy observations. Chang et al. proposed the use of adaptive wavelet thresholding for image denoising, by modeling the wavelet coefficients as a generalized Gaussian random variable, whose parameters are estimated locally (., within a given neighborhood). ii. Joint Probabilistic Model Hidden Markov Models (HMM) models are efficient in capturing interscale dependencies, whereas Random Markov Field models are more efficient to capture intrascale correlations. The plexity of loca。