We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented-stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.
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Tufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USATufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA
Almasi, Sepideh
Ben-Zvi, Ayal
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Harvard Med Sch, Dept Neurobiol, Boston, MA USA
Hebrew Univ Jerusalem, Inst Med Res IMRIC, Dept Dev Biol & Canc Res, Jerusalem, IsraelTufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA
Ben-Zvi, Ayal
Lacoste, Baptiste
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Univ Ottawa, Brain & Mind Res Inst, Ottawa Hosp Res Inst, Dept Cellular & Mol Med,Neurosci Program, Ottawa, ON, CanadaTufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA
Lacoste, Baptiste
Gu, Chenghua
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Harvard Med Sch, Dept Neurobiol, Boston, MA USATufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA
Gu, Chenghua
Miller, Eric L.
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Tufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USATufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA
Miller, Eric L.
Xu, Xiaoyin
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Brigham & Womens Hosp, Dept Radiol, Boston, MA USATufts Univ, Dept Elect & Comp Engn, Medford, MA 02155 USA