Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional

By Ola Marius Lysaker, Xuecheng Tai. International Journal of Computer Vision, 2005

317 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a5e51a11d91581288b061334b7184a66c151066b

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. Lei Zhang
2. Wotao Yin
3. Yin Zhang
4. Junfeng Yang
5. Guangming Shi
6. Weisheng Dong
7. Chunlin Wu
8. Xuecheng Tai

In Computer Vision and Pattern Recognition

1. Lei Zhang
2. Wotao Yin
3. Ming-Hsuan Yang
4. Wangmeng Zuo
5. Luc Van Gool
6. Yin Zhang
7. Guangming Shi
8. Weisheng Dong

Citations in the corpus

• Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, background): “The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Yongyi Yang.”
• A New Alternating Minimization Algorithm for Total Variation Image Reconstruction (2008, background): “Such examples arise in both image processing (e.g., [ 28 , 8]) and compres-”
• A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration (2009, background): “, [12, 23], which indicates the effect of preventing staircasing caused by TV.”
• Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling (2013, background): “widely used regularizer is the total variation (TV) model [10-13], which assumes that natural images have”
• Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models (2010, methodology): “The model immediately attracted much attention and has been extended to high order models [13, 46, 27, 29, 24, 35] and vectorial models for color image restoration [34, 2, 4, 14]; see [15] for an overview.”
• Implicit Neural Representations with Periodic Activation Functions (2020, methodology): “7, where we compare performance on texture images versus Deep Image Prior [41], Navier-Stokes, Fluid Dynamics Image Inpainting [7] (Diffusion), and SIRENs with no prior, total variation prior (TV), and Frobenius norm of Hessian [31, 30] priors (FH) respectively.”