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Found 8 papers, 0 papers with code
Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge
Our spectral clustering is generic, i. e. assuming nothing specific of the block model or streaming, used to generate the graphs for the Challenge, in contrast to the base code.
Signal reconstruction via operator guiding
Signal reconstruction from a sample using an orthogonal projector onto a guiding subspace is theoretically well justified, but may be difficult to practically implement.
Guided Signal Reconstruction Theory
New frame-less reconstruction methods are proposed, based on a novel concept of a reconstruction set, defined as a shortest pathway between the sample consistent set and the guiding set.
Accelerated graph-based nonlinear denoising filters
Denoising filters, such as bilateral, guided, and total variation filters, applied to images on general graphs may require repeated application if noise is not small enough.
Edge-enhancing Filters with Negative Weights
In [DOI:10. 1109/ICMEW. 2014. 6890711], a graph-based denoising is performed by projecting the noisy image to a lower dimensional Krylov subspace of the graph Laplacian, constructed using nonnegative weights determined by distances between image data corresponding to image pixels.
Accelerated graph-based spectral polynomial filters
Graph-based spectral denoising is a low-pass filtering using the eigendecomposition of the graph Laplacian matrix of a noisy signal.
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
The most efficient signal edge-preserving smoothing filters, e. g., for denoising, are non-linear.
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
In 3D image/video acquisition, different views are often captured with varying noise levels across the views.
