86 citations · 532 across the 35 of their papers we have counts for
5 papers · 2 filters
Accelerated graph-based nonlinear denoising filters
Andrew Knyazev, Alexander Malyshev
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. We for…
Edge-enhancing Filters with Negative Weights
Andrew Knyazev
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 u…
Accelerated graph-based spectral polynomial filters
Andrew Knyazev, Alexander Malyshev
Graph-based spectral denoising is a low-pass filtering using the eigendecomposition of the graph Laplacian matrix of a noisy signal. Polynomial filtering avoids costly computation…
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
Dong Tian, Hassan Mansour, Andrew Knyazev +1
In 3D image/video acquisition, different views are often captured with varying noise levels across the views. In this paper, we propose a graph-based image enhancement technique th…
Conjugate Gradient Acceleration of Non-Linear Smoothing Filters
Andrew Knyazev, Alexander Malyshev
The most efficient signal edge-preserving smoothing filters, e.g., for denoising, are non-linear. Thus, their acceleration is challenging and is often performed in practice by tuni…