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.
Bibliographical noteFunding Information:
This work is supported by the Office of Naval Research (Program Manager, Dr. Beborah Nalchajian) under grant No. N000141612755 and the start-up funding received by Prof. Jiarong Hong from University of Minnesota . The authors also gratefully acknowledge the help from Mr. S. Santosh Kumar by providing spray-generated droplet hologram database.
© 2020 Elsevier Ltd
- Deep neural network
- Digital inline holography
- Image analysis
- Machine learning
- Particle size distribution