Convergence analysis of the Bregman method for the variational model of image denoising
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Publication:734333
DOI10.1016/j.acha.2009.05.002zbMath1194.68251OpenAlexW1974118075MaRDI QIDQ734333
Publication date: 20 October 2009
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.acha.2009.05.002
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Cites Work
- Unnamed Item
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- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Wavelets on the interval and fast wavelet transforms
- Minimax estimation via wavelet shrinkage
- Wavelet-based minimal-energy approach to image restoration
- Linearized Bregman iterations for compressed sensing
- Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
- Nonlocal Linear Image Regularization and Supervised Segmentation
- Image decomposition via the combination of sparse representations and a variational approach
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- An Iterative Regularization Method for Total Variation-Based Image Restoration
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