Sketched learning for image denoising
From MaRDI portal
Publication:826190
DOI10.1007/978-3-030-75549-2_23zbMath1484.68308OpenAlexW3128121082MaRDI QIDQ826190
Jean-François Aujol, Yann Traonmilin, Hui Shi
Publication date: 20 December 2021
Full work available at URL: https://doi.org/10.1007/978-3-030-75549-2_23
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Image analysis in multivariate analysis (62H35) Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items
Compressive Learning for Patch-Based Image Denoising ⋮ Sparse mixture models inspired by ANOVA decompositions
Cites Work
- Unnamed Item
- A mathematical introduction to compressive sensing
- Statistical guarantees for the EM algorithm: from population to sample-based analysis
- On the convergence properties of the EM algorithm
- Local minima and convergence in low-rank semidefinite programming
- Compressive statistical learning with random feature moments
- Statistical learning guarantees for compressive clustering and compressive mixture modeling
- SURE Guided Gaussian Mixture Image Denoising
- A Nonlocal Bayesian Image Denoising Algorithm
- Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a <inline-formula> <tex-math notation="LaTeX">$100{\times}$ </tex-math> </inline-formula> Speed-Up
- An improved data stream summary: the count-min sketch and its applications
- The basins of attraction of the global minimizers of the non-convex sparse spike estimation problem
- Image Denoising with Generalized Gaussian Mixture Model Patch Priors
- Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
- Sketching for large-scale learning of mixture models
- A Review of Image Denoising Algorithms, with a New One
