An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise

By Junfeng Yang, Yin Zhang, Wotao Yin. SIAM Journal on Scientific Computing, 2009

375 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/03c36e5c747e27f7a73a43c02dc314b8ac35a52b

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. Wotao Yin
2. Yin Zhang
3. Stanley J. Osher
4. Yangyang Xu
5. Xiaoqun Zhang
6. Martin Burger
7. Zaiwen Wen
8. Paul Tseng

In Computer Vision and Pattern Recognition

1. Wotao Yin
2. Yin Zhang
3. Stanley J. Osher
4. Lei Zhang
5. Jiaya Jia
6. Ming-Hsuan Yang
7. Li Xu
8. Xiaoqun Zhang

Citations in the corpus

• 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.”
• An Augmented Lagrangian Method for Total Variation Video Restoration (2011, methodology): “The proposed algorithm belongs to the family of operator splitting methods [ 10 ], [11], [31].”
• A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration (2010, methodology): “A similar splitting approach is also applied for TV−L1 minimization in [32, 48].”
• An alternating direction algorithm for matrix completion with nonnegative factors (2011, methodology): “In fact, the proposed algorithm in this paper extends the work in [34], and both algorithms are based on the algorithm of alternating direction method of multipliers (ADM) [12, 11, 2, 28, 32, 30].”
• Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization (2014, methodology): “The approach of ADM has been widely used in solving convex optimization and related problems, including problems arising from partial differential equations (PDEs) [20, 23], variational inequality problems [26, 27], conic programming [14, 41], various nonlinear convex programming [1, 11, 28, 29, 38]”
• Two-Phase Kernel Estimation for Robust Motion Deblurring (2010, methodology): “We propose solving it using an efficient alternating minimization method, based on a half-quadratic splitting for 1 minimization [16, 18].”
• Approximation accuracy, gradient methods, and error bound for structured convex optimization (2010, methodology): “Linear convergence of the block-coordinate minimization methods used in [147] for TV-regularized image reconstruction, with f(w, u) = 1 2‖w−Bu‖(2)2+ γ 2‖Au−b‖(2)2, P (w, u) = ∑ J ‖wJ‖2, and in [152] for TV-regularized image deblurring, with f(w, u, z) = 1 2‖w − Bu‖(2)2 + γ 2 ‖Au − b − z‖(2)2, P (w, ”
• Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications (2016, methodology): “Because of the explicit use of the forward and the prior models, MAP estimation is also a model-based image reconstruction (MBIR) method [2] which has many important applications in deblurring [3]–[5], interpolation [6]–[8], super-resolution [9]–[12] and computed tomography [13], to name a few.”