The Steerable Graph Laplacian and its Application to Filtering Image Datasets
From MaRDI portal
Publication:5230412
DOI10.1137/18M1169394zbMath1452.68253arXiv1802.01894OpenAlexW2963142433WikidataQ129160427 ScholiaQ129160427MaRDI QIDQ5230412
Publication date: 22 August 2019
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1802.01894
Computing methodologies for image processing (68U10) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (5)
Symmetric Measures, Continuous Networks, and Dynamics ⋮ Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit ⋮ Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling ⋮ An Adaptive Multigrid Method Based on Path Cover ⋮ Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise
Cites Work
- Unnamed Item
- Unnamed Item
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- On information plus noise kernel random matrices
- Prolate spheroidal wave functions on a disc -- integration and approximation of two-dimensional bandlimited functions
- Towards a theoretical foundation for Laplacian-based manifold methods
- A moment-based nonlocal-means algorithm for image denoising
- Perturbation of the eigenvectors of the graph Laplacian: application to image denoising
- Approximation scheme for essentially bandlimited and space-concentrated functions on a disk
- Diffusion maps
- From graph to manifold Laplacian: the convergence rate
- Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
- Two-Dimensional Tomography from Noisy Projections Taken at Unknown Random Directions
- Vector diffusion maps and the connection Laplacian
- Viewing Angle Classification of Cryo-Electron Microscopy Images Using Eigenvectors
- On the Optimality of Shape and Data Representation in the Spectral Domain
- Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A Unified Framework
- Low Dimensional Manifold Model for Image Processing
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Matrix decompositions using sub-Gaussian random matrices
- Steerable Principal Components for Space-Frequency Localized Images
- Graph Laplacian Tomography From Unknown Random Projections
- Grids and transforms for band-limited functions in a disk
- Prolate Spheroidal Wave Functions, Fourier Analysis and Uncertainty - IV: Extensions to Many Dimensions; Generalized Prolate Spheroidal Functions
- Graph connection Laplacian methods can be made robust to noise
This page was built for publication: The Steerable Graph Laplacian and its Application to Filtering Image Datasets