Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency

By Levent Sendur, Ivan W. Selesnick. IEEE Transactions on Signal Processing, 2002

1,082 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/601e7ffddb8dcb87e6c5d21aec32789d9864dfe6

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage (2011, background): “The denoising of PCA low-energy output channels can be done by using Sendur and Selesnick’s bivariate wavelet thresholding [11] because this method is one of the best image denoising methods in the literature.”
Image Denoising Using the Higher Order Singular Value Decomposition (2013, neutral): “There exist several more sophisticated methods to manipulate wavelet coefficients, such as those that exploit dependencies in transform coefficients at the same spatial location but at different scales (e.g., in [29] or the BLS-GSM method in [25]) or at adjacent spatial locations [30].”
Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization (2009, background): “…1999; Wainwright & Simoncelli, 2000; Huang & Mumford, 1999; Parra, Spence, & Sajda, 2001; Hyv¨arinen, Hoyer, & Inki, 2001; Srivastava, Liu, & Grenander, 2002; Sendur & Selesnick, 2002; Portilla, Strela, Wainwright, & Simoncelli, 2003; Teh, Welling, & Osindero, 2003; Gehler & Welling, 2006).”
Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures (2009, methodology): “A variety of parametric models have been proposed to capture these regularities, including the generalized Laplacian [7], [8], [9], [10], the Bessel K [11], the multi-variate Student’s t-distribution [12], the α-stable family [13] and the Cauchy distribution [14].”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “In [28], 2D bivariate wavelet shrinkage (2D BiShrink) [29] was extended to 3D for the purpose of HSI denoising.”
Centralized sparse representation for image restoration (2011, background): “Actually, in wavelet based image denoising [26], the connection between wavelet representation and Bayesian framework has been well established.”
Sparsity-based image denoising via dictionary learning and structural clustering (2011, background): “In the literature of wavelet thresholding [16], the connection between sparse representation and Bayesian denoising has been well established.”
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser (2020, background): “, [1–7]), they are too simplistic to generate complex features that occur in our visual world, or to solve more demanding statistical inference problems.”
The SURE-LET Approach to Image Denoising (2007, methodology): “[3], [4]:3 The authors’ method, called BiShrink, is based on new non-Gaussian bivariate distributions to model interscale dependencies.”
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding (2007, methodology): “" • Sendur et al. [11], [12]: 3 Their method, called BiShrink, is based on new non-Gaussian bivariate distributions Fig.”
Wavelets on Graphs via Spectral Graph Theory (2009, positive): “These expressions will provide an analogue that we will later use to deﬁne the Spectral Graph Wavelet Transform.”