RWRM: residual Wasserstein regularization model for image restoration

DOI10.3934/IPI.2020069OpenAlexW3094949480WikidataQ113053484 ScholiaQ113053484MaRDI QIDQ2063015

Ruiqiang He, Hua Huang, Bingzhe Wei, Xiang Chu Feng, Xiao-Long Zhu

Publication date: 10 January 2022

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/ipi.2020069

zbMATH Keywords

Wasserstein distancenon-blind deconvolutionGaussian denoisingresidual histogram

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10)

Cites Work

• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• Image recovery via nonlocal operators
• Variational methods in imaging
• An algorithm for total variation minimization and applications
• Convex Variational Image Restoration with Histogram Priors
• Convergence in the Wasserstein Metric for Markov Chain Monte Carlo Algorithms with Applications to Image Restoration
• Wasserstein Loss for Image Synthesis and Restoration
• Nonlocal Operators with Applications to Image Processing
• Markov Random Field Modeling in Image Analysis
• Image denoising using scale mixtures of gaussians in the wavelet domain
• Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
• Edge-preserving and scale-dependent properties of total variation regularization
• Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising
• Single Image Interpolation Via Adaptive Nonlocal Sparsity-Based Modeling
• Texture Reconstruction Guided by a High-Resolution Patch
• Low Dimensional Manifold Model for Image Processing
• Nonlocally Centralized Sparse Representation for Image Restoration
• Signal Recovery by Proximal Forward-Backward Splitting
• An Iterative Regularization Method for Total Variation-Based Image Restoration
• Optimal Transport
• Compressed sensing

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