Multiplicative noise removal via using nonconvex regularizers based on total variation and wavelet frame
From MaRDI portal
Publication:2297111
DOI10.1016/j.cam.2019.112684zbMath1493.94007OpenAlexW2997600526MaRDI QIDQ2297111
Zemin Ren, Chun-Yan Li, Liming Tang
Publication date: 18 February 2020
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2019.112684
total variationmultiplicative noisewavelet framealternating minimization methodnonconvex regularizer
Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Numerical optimization and variational techniques (65K10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A hybrid optimization method for multiplicative noise and blur removal
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- An efficient nonconvex regularization for wavelet frame and total variation based image restoration
- Primal-dual algorithm based on Gauss-Seidel scheme with application to multiplicative noise removal
- A convex total generalized variation regularized model for multiplicative noise and blur removal
- Multiplicative noise removal in imaging: an exp-model and its fixed-point proximity algorithm
- Removing multiplicative noise by Douglas-Rachford splitting methods
- On gradients of functions definable in o-minimal structures
- Affine systems in \(L_ 2(\mathbb{R}^d)\): The analysis of the analysis operator
- Framelets: MRA-based constructions of wavelet frames
- Multiplicative noise removal with a sparsity-aware optimization model
- A fast minimization method for blur and multiplicative noise removal
- A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise
- A New Convex Optimization Model for Multiplicative Noise and Blur Removal
- Smoothing Nonlinear Conjugate Gradient Method for Image Restoration Using Nonsmooth Nonconvex Minimization
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
- A New Total Variation Method for Multiplicative Noise Removal
- Linearized Bregman Iterations for Frame-Based Image Deblurring
- Compressed sensing recovery via nonconvex shrinkage penalties
- Clarke Subgradients of Stratifiable Functions
- A Variational Approach to Removing Multiplicative Noise
- Split Bregman Methods and Frame Based Image Restoration
- Variational Analysis
- Multiplicative Noise and Blur Removal by Framelet Decomposition and <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math> </inline-formula>-Based L-Curve Method
- Multiplicative noise removal via combining total variation and wavelet frame
- Multiplicative Noise Removal with Spatially Varying Regularization Parameters
- Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction
- A New Multiplicative Denoising Variational Model Based on $m$th Root Transformation
- New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
- Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
- Convergence of the Iterates of Descent Methods for Analytic Cost Functions
- Analysis of the Recovery of Edges in Images and Signals by Minimizing Nonconvex Regularized Least-Squares
