Augmented Lagrangian method for an Euler's elastica based segmentation model that promotes convex contours
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Publication:507251
DOI10.3934/ipi.2017001zbMath1416.94013OpenAlexW2574999669MaRDI QIDQ507251
Xue-Cheng Tai, Egil Bae, Wei Zhu
Publication date: 3 February 2017
Published in: Inverse Problems and Imaging (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/ipi.2017001
Numerical optimization and variational techniques (65K10) Numerical methods based on necessary conditions (49M05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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Cites Work
- Unnamed Item
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- Nonlinear total variation based noise removal algorithms
- Image segmentation using Euler's elastica as the regularization
- Filtering, segmentation and depth
- Homotopy method for a mean curvature-based denoising model
- Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
- Geodesic active contours
- A direct variational approach to a problem arising in image reconstruction
- Segmentation with depth but without detecting junctions
- A PDE-based fast local level set method
- Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional
- Using prior shapes in geometric active contours in a variational framework
- Level set methods and dynamic implicit surfaces
- A linear framework for region-based image segmentation and inpainting involving curvature penalization
- A variational model for capturing illusory contours using curvature
- Augmented Lagrangian method for a mean curvature based image denoising model
- Image Denoising Using Mean Curvature of Image Surface
- A Fast Algorithm for Euler's Elastica Model Using Augmented Lagrangian Method
- Optimal approximations by piecewise smooth functions and associated variational problems
- Approximation of functional depending on jumps by elliptic functional via t-convergence
- Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- Analogue of the Total Variation Denoising Model in the Context of Geometry Processing
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- Active contours without edges
- Euler's Elastica and Curvature-Based Inpainting
- Functional-analytic and numerical issues in splitting methods for total variation-based image reconstruction
- A New Augmented Lagrangian Approach for $L^1$-mean Curvature Image Denoising
- A Convex, Lower Semicontinuous Approximation of Euler's Elastica Energy
- Aspects of Total Variation RegularizedL1Function Approximation
- Graph Cuts for Curvature Based Image Denoising
- Multigrid Algorithm for High Order Denoising
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