Abstract:

In [doi:10.1109/ICMEW.2014.6890711], a graphbased denoising is performed by projecting the noisy image to a lower dimensional Krylov subspace of the graph Laplacian, constructed using non-negative 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 graphbased signal processing, e.g., denoising, compared to the standard construction, without affecting the computational costs.