Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In [D01:10.1109/ICMEW.2014.6890711], a graph-based denoising is performed by projecting the noisy image to a lor dimensional Krylov subspace of the graph Laplacian, constructed using nonnegative ights determined by distances beten image data corresponding to image pixels.  extend the construction of the graph Laplacian to the case, where some graph ights 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.
['Andrew V. Knyazev']
I am writing a research paper on a idea Edge-enhancing filters with negative weights
[{"from": "human", "value": "I am writing a research paper on a idea Edge-enhancing filters with negative weights"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In [D01:10.1109/ICMEW.2014.6890711], a graph-based denoising is performed by projecting the noisy image to a lor dimensional Krylov subspace of the graph Laplacian, constructed using nonnegative ights determined by distances beten image data corresponding to image pixels.  extend the construction of the graph Laplacian to the case, where some graph ights 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."}]