Bivariate shrinkage with local variance estimation

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

656 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/ea21dbebe6459a6a8dec184d83d44bbfb23373ec

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In Analytical Chemistry

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

The dual-tree complex wavelet transform (2005, methodology): “Denoising with the data-driven locally adaptive bishrink algorithm of [91] was performed using both the critically sampled separable DWT and the dual-tree CWT.”
Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage (2011, methodology): “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 with block-matching and 3D filtering (2006, methodology): “We use two buffers of the same size–ebuff for estimates and wbuff for weights–to represent the summations in the numerator and denominator, respectively, in (5).”
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding (2007, methodology): “Yet, we will show that our denoising method performs better than the nonredundant versions of the state-of-the-art methods [9], [10], [12] on almost all tested images, to the noteworthy exception of Barbara, which may require intra-scale processing.”
Directional multiscale modeling of images using the contourlet transform (2006, neutral): “, [31], [32], [28]), have been developed.”
Sparse directional image representations using the discrete shearlet transform (2008, methodology): “They include several schemes of \directional wavelets" (such as [1,3]), contourlets [12,31], complex wavelets [25, 34 ] brushlets [9], ridgelets [6], curvelets [7], bandelets [29] and shearlets [20,18], introduced by the authors and their collaborators.”
Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms (2008, methodology): “The reader who is interested in state-of-the-art methods is referred to the relevant literature; in particular the BiShrink (dual tree complex wavelet decompo sition) [50], BLS-GSM (full steerable pyramidal decomposition) [51], ProbShrink (undecimated D aubechies symlets) [52], and SURELET (with red”
The SURE-LET Approach to Image Denoising (2007, methodology): “50 Method House 256 × 256 BiShrink [84] 37.”