A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration

By Junfeng Yang, Wotao Yin, Yin Zhang, Yilun Wang. SIAM Journal of Imaging Sciences, 2009

533 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0d7352711c21e0cb669825e45cb58942c44df863

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Yin Zhang
2. Chengbo Li
3. Wotao Yin
4. Junfeng Yang
5. Chunlin Wu
6. Xuecheng Tai
7. Yun Raymond Fu
8. Ming Shao

In Computer Vision and Pattern Recognition

1. Yin Zhang
2. Wotao Yin
3. Chengbo Li
4. Junfeng Yang
5. Chunlin Wu
6. Xuecheng Tai
7. Yun Raymond Fu
8. Ming Shao

Citations in the corpus

• Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models (2010, methodology): “The other is based on variable-splitting and equality constrained optimization, e.g., the approach proposed in [39, 40, 42] which uses alternative minimization of the penalized cost functional, and the method in [25] where splitting is applied to the data (cid:12)delity term, as well as split…”
• An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing (2010, background): “For instance, measurement matrices A and GA in ℓ 1 minimization should result in exactly the same recoverability and stability as long as matrix G is square and nonsingular, but their RIP could vary a lot.”
• Optical flow modeling and computation: A survey (2015, methodology): “The optimization of the data term with v fixed is efficiently performed by a thresholding scheme [280], and fixing w yields the Rudin-Osher-Fatemi model [210], optimized with the duality”
• Group sparse optimization by alternating direction method (2013, unknown): “i=1 [ wi‖zgi‖2 + β1 2 ‖zgi − xgi − 1 β1 (λ1)gi‖(2)2 ] , (11)”
• An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise (2009, methodology): “A derivation based on a smoothed approximation of TV and the additive form of half-quadratic technique [29] can be found in [51] for a deblurring model under Gaussian noise.”
• Generalized Transfer Subspace Learning Through Low-Rank Constraint (2014, background): “Step (2) can be solved via singular value thresholding (SVT) (Cai et al. 2010) and step (4) is solved by Lemma 3.3 (Yang et al. 2009).”
• A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data (2010, methodology): “Similar ideas were applied to multichannel deconvolution in [ 43 ] and impulsive noise removal in [44].”