A domain decomposition Fourier continuation method for enhanced \(L_1\) regularization using sparsity of edges in reconstructing Fourier data
From MaRDI portal
Publication:1742671
DOI10.1007/S10915-017-0467-YOpenAlexW2619190000MaRDI QIDQ1742671
Publication date: 12 April 2018
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-017-0467-y
domain decompositionGibbs phenomenonsparsityFourier continuationFourier reconstructionconvex optimization with \(L_1\) minimization
Uses Software
Cites Work
- Unnamed Item
- Image reconstruction from undersampled Fourier data using the polynomial annihilation transform
- Multi-domain Fourier-continuation/WENO hybrid solver for conservation laws
- Hybrid Fourier-continuation method and weighted essentially non-oscillatory finite difference scheme for hyperbolic conservation laws in a single-domain framework
- Image reconstruction from Fourier data using sparsity of edges
- Family of spectral filters for discontinuous problems
- Gram polynomials and the Kummer function
- A comparison of numerical algorithms for Fourier extension of the first, second, and third kinds
- A Fourier continuation method for the solution of elliptic eigenvalue problems in general domains
- High-order unconditionally stable FC-AD solvers for general smooth domains. I: Basic elements
- Accurate, high-order representation of complex three-dimensional surfaces via Fourier continuation analysis
- Discrete Periodic Extension Using an Approximate Step Function
- Spectral Methods for Time-Dependent Problems
- Improved Total Variation-Type Regularization Using Higher Order Edge Detectors
- Learning Sparsifying Transforms
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images
This page was built for publication: A domain decomposition Fourier continuation method for enhanced \(L_1\) regularization using sparsity of edges in reconstructing Fourier data
