期刊
CHEMICAL ENGINEERING SCIENCE
卷 225, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2020.115830
关键词
Image analysis; Machine learning; Deep neural network; Particle size distribution; Digital inline holography
资金
- Office of Naval Research [N000141612755]
- University of Minnesota
- U.S. Department of Defense (DOD) [N000141612755] Funding Source: U.S. Department of Defense (DOD)
We propose a learning-based image processing method for particle size measurement based on digital holography in this paper. The proposed approach uses a modified U-net architecture with recorded holograms, hologram reconstructed to each longitudinal location, and minimum intensity projection in longitudinal direction as inputs to produce outputs consisting of in-focus particles at each longitudinal location and their 2D centroids. A soft generalized dice loss is used for the particle size channel and a total variation regularized mean squared error loss is employed for the 2D centroids channel. The proposed method has been assessed using synthetic, manually-labeled experimental, and real experimental holograms. The results demonstrate that our approach have better performance in comparison to the state-of-the-art non-machine-learning methods in terms of particle extraction rate and positioning accuracy. Our learning-based approach can be readily extended to other types of image-based particle size measurement tasks such as shadowgraph imaging and defocusing imaging. (C) 2020 Elsevier Ltd. All rights reserved.
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