Image denoising by generalized total variation regularization and least squares fidelity-Reference-Cited by-同舟云学术

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Image denoising by generalized total variation regularization and least squares fidelity

Published:2013-10-04 Issue:1 Volume:26 Page:243-266
ISSN:0923-6082
Container-title:Multidimensional Systems and Signal Processing
language:en
Short-container-title:Multidim Syst Sign Process

Author:

Yan Jie,Lu Wu-Sheng

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing,Software

Link

http://link.springer.com/content/pdf/10.1007/s11045-013-0255-2.pdf

Reference33 articles.

1. Allard, W. K. (2008). Total variation regularization for image denoising, ii. examples. SIAM Journal on Imaging Sciences, 1(4), 400–417.

2. Allard, W. K. (2009). Total variation regularization for image denoising, iii. examples. SIAM Journal on Imaging Sciences, 2(2), 532–568.

3. Beck, A., & Teboulle, M. (2009). Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. Image Processing, IEEE Transactions on, 18(11), 2419–2434.

4. Candès, E., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.

5. Candes, E., Wakin, M., & Boyd, S. (2008). Enhancing sparsity by reweighted $$\ell _1$$ ℓ 1 minimization. Journal of Fourier Analysis and Applications, 14(5), 877–905.

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