Speckle noise removal via learned variational models
From MaRDI portal
Publication:6546946
DOI10.1016/j.apnum.2023.06.002zbMATH Open1540.94008MaRDI QIDQ6546946
Salvatore Cuomo, Mariapia De Rosa, Monica Pragliola, Stefano Izzo, Francesco Piccialli
Publication date: 30 May 2024
Published in: Applied Numerical Mathematics (Search for Journal in Brave)
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Cites Work
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nonlinear total variation based noise removal algorithms
- Modification of TV-ROF denoising model based on split Bregman iterations
- Cauchy noise removal by nonconvex ADMM with convergence guarantees
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- Constrained TV\(_p\)-\(\ell_2\) model for image restoration
- A Variational Approach to Removing Multiplicative Noise
- Split Bregman Methods and Frame Based Image Restoration
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Learning regularization parameters of inverse problems via deep neural networks
- Multiplicative Noise Removal with Spatially Varying Regularization Parameters
- Introduction to Bayesian Scientific Computing
- Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling
- On and Beyond Total Variation Regularization in Imaging: The Role of Space Variance
- Multiplicative Noise Removal for Texture Images Based on Adaptive Anisotropic Fractional Diffusion Equations
- Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
This page was built for publication: Speckle noise removal via learned variational models
