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arXiv · 1509.02491

Edge-enhancing Filters with Negative Weights

Andrew Knyazev
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Abstract

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. We~extend the construction of the graph Laplacian to the case, where some graph weights can be negative. Removing the positivity constraint provides a more accurate inference of a graph model behind the data, and thus can improve quality of filters for graph-based signal processing, e.g., denoising, compared to the standard construction, without affecting the costs.

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Andrew Knyazev. 2015-09-08. Edge-enhancing Filters with Negative Weights. https://doi.org/10.1109/globalsip.2015.7418197

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