A first-order image restoration model that promotes image contrast preservation
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Publication:2051048
DOI10.1007/s10915-021-01557-1zbMath1471.94006OpenAlexW3178838833WikidataQ113106884 ScholiaQ113106884MaRDI QIDQ2051048
Publication date: 1 September 2021
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-021-01557-1
Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical methods for inverse problems for integral equations (65R32)
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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
- Augmented Lagrangian method for an Euler's elastica based segmentation model that promotes convex contours
- The total variation flow in \(\mathbb R^N\)
- Image recovery via total variation minimization and related problems
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Image denoising using \(L^p\)-norm of mean curvature of image surface
- A first-order image denoising model for staircase reduction
- An adaptive algorithm for TV-based model of three norms \(L_q\) \((q = \frac{1}{2}, 1, 2)\) in image restoration
- A numerical study of a mean curvature denoising model using a novel augmented Lagrangian method
- Augmented Lagrangian method for a mean curvature based image denoising model
- Mathematical Models for Local Nontexture Inpaintings
- 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
- The Split Bregman Method for L1-Regularized Problems
- Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time
- Noise Removal Using Smoothed Normals and Surface Fitting
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- A Variational Method in Image Recovery
- Image Decomposition and Restoration Using Total Variation Minimization and theH1
- Edge-preserving and scale-dependent properties of total variation regularization
- High-Order Total Variation-Based Image Restoration
- First-Order Methods in Optimization
- Aspects of Total Variation RegularizedL1Function Approximation
- Handbook of Mathematical Models in Computer Vision
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Multigrid Algorithm for High Order Denoising
- Total Generalized Variation
- A study in the BV space of a denoising-deblurring variational problem
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