What is it about?
This is a novel denoising filter for flow visualization data. It uses not only singular value decomposiion but also a "split and overlap" method. This makes the denosing filter unique and novel. By using the "split-and-overlap" method , you no longer have to consider at which basis you may cut off when reconstructuring the data after decomposition.
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Why is it important?
1. The filter is faster than a conventional divergence-free filter by 100 times. 2. Nevertheless its denoising performances is as good as the divergence-free filter. 3. You would need onyl the first basis when reconstructing the data after decomposition.
Perspectives
The filter is useful and helpful when denosing data obtained by 4D flow MRI, PIV, PTV and so on. Additionally it can be applied to any other form of 2D or 3D data matrix with random noises.
Simon Song
Hanyang University
Read the Original
This page is a summary of: Denoising four-dimensional flow magnetic resonance imaging data using a split-and-overlap approach via singular value decomposition, Physics of Fluids, January 2024, American Institute of Physics,
DOI: 10.1063/5.0180996.
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