Adaptive wavelet thresholding for image denoising and compression

By S. Chang, Bin Yu, Martin Vetterli. IEEE Transactions on Image Processing, 2000

3,276 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/04f7769cf0908101af532b02428928baccb2c4d3

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Citations in the corpus

A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding (2007, methodology): “• For the Barbara image, our method is among the worst performers together with the pointwise BayesShrink.”
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency (2002, methodology): “Example 2 In this example, a subband adaptive data-driven image denoising system that uses Model 3 given will be described, and some comparison to VisuShrink [14], SureShrink [15], BayesShrink [4], and the hidden Markov tree model [11] will be given.”
Wavelets on Graphs via Spectral Graph Theory (2009, positive): “This tool is provided by Spectral Graph Theory.”
Bivariate shrinkage with local variance estimation (2002, methodology): “In [2], the simple soft-thresholding idea is used for each of the wavelet subbands, and the threshold value is estimated to minimize the mean-square error.”
Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage (2006, methodology): “The ideal risks of these two oracles are given by (7)”
Directionlets: anisotropic multidirectional representation with separable filtering (2006, methodology): “The standard deviation σ of the noise is either known a priori or can be estimated using the robust estimator [14; 15; 20; 30; 32]”
A Survey of Black-Box Adversarial Attacks on Computer Vision Models (2019, background): “Prior works [75–77] illustrate certain regularities in wavelet responses of natural images that can be used to denoise images.”
Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding (2003, methodology): “[23] and the hard thresholding scheme of Pan et al.”
Optimal selection of wavelet basis function applied to ECG signal denoising (2006, background): “threshold and the Sure threshold and has been shown to perform well [25].”
Why Simple Shrinkage Is Still Relevant for Redundant Representations? (2006, background): “, hidden Markov models), could give a substantial improvement in the denoising effect [28]–[32], [34]–[39].”
Noise2Self: Blind Denoising by Self-Supervision (2019, background): “Figure 1, we show the corresponding loss curves for J -invariant versions of a wavelet filter, where we tune the threshold σ, and NL-means, where we tune a cut-off distance h (Buades et al., 2005a; Chang et al., 2000; van der Walt et al., 2014).”
Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes (2017, neutral): “[13-15].”
Differential Privacy via Wavelet Transforms (2009, methodology): “, [13], [14], [15], [16], [17]) for choosing an appropriate based only on the noisy wavelet coefficients.”
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising (2015, methodology): “Chang et al. modeled the wavelet transform coefficients as generalized Gaussian distribution, and proposed the BayesShrink [6] algorithm.”
SAR speckle reduction using wavelet denoising and Markov random field modeling (2002, methodology): “Under certain conditions [12], [13], speckle in SAR images is generally assumed to be fully developed and modeled as multiplicative random noise, whereas most of the existing wavelet denoising algorithms are developed for additive white Gaussian noise (AWGN) [11], [14]–[16], as additive noise is mos”
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications (2006, methodology): “Thus, most state-of-the-art wavelet denoising algorithms (see for example [6]–[8]) use an expansion with less shift sensitivity than the standard maximally decimated wavelet decomposition—the most common being the nonsubsampled wavelet transform (NSWT) computed with the à trous algorithm [9].”
Deep Convolutional Neural Network for Inverse Problems in Imaging (2016, background): “2713099 problems in imaging including denoising [1]–[3], deconvolution [4], [5], and interpolation [6].”