Noise reduction with a multiscale edge representation and perceptual criteria

Lü, Jian; Weaver, John B.; Healy, Dennis M.; Xu, Ying

doi:10.1109/tftsa.1992.274118

Cited by 31 publications

(19 citation statements)

References 14 publications

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“…Wavelet smoothing techniques capitalize on the different properties of signal and noise wavelet components. Techniques such as thresholding or shrinkage of the noisy wavelet coefficients, and reconstruction from the local minima of the wavelet transform, have been successfully used in a variety of denoising problems [11][12][13][14][15][16][17][18]. In the spectrum estimation problem, the wavelet coefficients of the additive noise are non-Gaussian and mutually dependent.…”

Section: Introductionmentioning

confidence: 99%

Wavelet thresholding techniques for power spectrum estimation

Moulin

1994

IEEE Trans. Signal Process.

143

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Estimation of the power spectrum S( f ) of a stationary random process can be viewed as a nonparametric statistical estimation problem. We introduce a nonparametric approach based on a wavelet representation for the logarithm of the unknown S( f ). This approach offers the ability to capture statistically significant components of lnS( f ) at different resolution levels and guarantees nonnegativity of the spectrum estimator. The spectrum estimation problem is set up as a problem of inference on the wavelet coefficients of a signal corrupted by additive non-Gaussian noise. We propose a wavelet thresholding technique to solve this problem under specified noise/resolution tradeoffs and show that the wavelet coefficients of the additive noise may be treated as independent random variables. The thresholds are computed using a saddle-point approximation to the distribution of the noise coefficients.

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Section: Introductionmentioning

confidence: 99%

Wavelet thresholding techniques for power spectrum estimation

Moulin

1994

IEEE Trans. Signal Process.

143

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“…Using the mobility scalogram, temporal denoising has been applied through the implementation of the modulus maxima-based method. Here, the energy in the scalogram is redistributed to its maximal modulus turning points with respect to each scale [65]. By following these Time (s) Figure 12.…”

Section: The Mobility Scalogram Modulus Maxima Temporal Filteringmentioning

confidence: 99%

Low-Oscillation Complex Wavelets

Addison

Watson

Tian³

2002

Journal of Sound and Vibration

115

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“…This 2-D wavelet transform requires two wavelets, namely, and . At a particular scale we have (1) The dyadic wavelet transform , at a scale has two components given by (2) Therefore, the multiresolution wavelet coefficients are…”

Section: Wavelet Transform In Two Dimensionsmentioning

confidence: 99%

“…reconstruction process is based on an interactive projection procedure, which may be computationally demanding. Lu et al [2] have proposed using wavelets for image filtering and edge detection. In their approach, local maxima are tracked in scale-space, and represented by a tree structure.…”

mentioning

confidence: 99%

Adaptive image denoising using scale and space consistency

Scharcanski

Jung

Clarke

2002

IEEE Trans. on Image Process.

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Abstract-This paper proposes a new method for image denoising with edge preservation, based on image multiresolution decomposition by a redundant wavelet transform. In our approach, edges are implicitly located and preserved in the wavelet domain, whilst image noise is filtered out. At each resolution level, the image edges are estimated by gradient magnitudes (obtained from the wavelet coefficients), which are modeled probabilistically, and a shrinkage function is assembled based on the model obtained. Joint use of space and scale consistency is applied for better preservation of edges. The shrinkage functions are combined to preserve edges that appear simultaneously at several resolutions, and geometric constraints are applied to preserve edges that are not isolated. The proposed technique produces a filtered version of the original image, where homogeneous regions appear separated by well-defined edges. Possible applications include image presegmentation, and image denoising.

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Noise reduction with a multiscale edge representation and perceptual criteria

Cited by 31 publications

References 14 publications

Wavelet thresholding techniques for power spectrum estimation

Wavelet thresholding techniques for power spectrum estimation

Low-Oscillation Complex Wavelets

Adaptive image denoising using scale and space consistency

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