A Variational Approach to Additive Image Decomposition into Structure, Harmonic, and Oscillatory Components
From MaRDI portal
Publication:5024385
DOI10.1137/20M1355987zbMath1478.65065OpenAlexW3214945181MaRDI QIDQ5024385
Martin Huska, Sung Ha Kang, Alessandro Lanza, Serena Morigi
Publication date: 31 January 2022
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/20m1355987
Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)
Related Items (4)
Ternary image decomposition with automatic parameter selection via auto- and cross-correlation ⋮ Fast and stable schemes for non-linear osmosis filtering ⋮ Implicit regularization effects of the Sobolev norms in image processing ⋮ Parameter-free restoration of piecewise smooth images
Cites Work
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- Nonlinear total variation based noise removal algorithms
- Nonconvex nonsmooth optimization via convex-nonconvex majorization-minimization
- Image restoration and decomposition via bounded total variation and negative Hilbert-Sobolev spaces
- Modeling textures with total variation minimization and oscillating patterns in image processing
- Convex non-convex segmentation of scalar fields over arbitrary triangulated surfaces
- Non-convex and non-smooth variational decomposition for image restoration
- Convex non-convex image segmentation
- Structure-texture image decomposition -- modeling, algorithms, and parameter selection
- Image decomposition into a bounded variation component and an oscillating component
- Dual norms and image decomposition models
- Convex Image Denoising via Non-Convex Regularization
- Image Decomposition and Restoration Using Total Variation Minimization and theH1
- A Note on Antireflective Boundary Conditions and Fast Deblurring Models
- Sparse Signal Estimation by Maximally Sparse Convex Optimization
- Energy Minimization Methods
- A convex-nonconvex variational method for the additive decomposition of functions on surfaces
- A PDE Formalization of Retinex Theory
- Hessian Schatten-Norm Regularization for Linear Inverse Problems
- Image Decomposition Using Total Variation and div(BMO)
This page was built for publication: A Variational Approach to Additive Image Decomposition into Structure, Harmonic, and Oscillatory Components