For the shadowgraphy techniques with a single camera, it is difficult to accurately obtain the shape, size, and depth location of the droplets out of focus due to the defocus blur. This paper proposed a deep learning-based method to recover the sharp images and infer the depth information from the defocused blur droplets images. The proposed model comprising of a defocus map estimation subnetwork and a defocus deblur subnetwork is optimized with a two-stage strategy. To train the networks, the synthetic blur data generated by the Gauss kernel method are utilized as the input data, which mimic the defocused images of droplets. The proposed approach has been assessed based on synthetic images and real sphere blur images. The results demonstrate that our method has satisfactory performance both in terms of depth location estimation and droplet size measurement, e.g., the diameter relative error is less than 5% and the location error is less than 1 mm for the sphere with a diameter of more than 1 mm. Moreover, the present model also exhibits considerable generalization and robustness against the transparent ellipsoid and the random background noise. A further application of the present model to the measurement of transparent water droplets generated by an injector is also explored and illustrates the practicability of the present model in real experiments. The present study indicates that the proposed learning-based method is promising for the three-dimensional (3D) measurement of spray droplets via a combination of shadowgraphy techniques using a single camera, which will greatly reduce experimental costs and complexity.
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July 2022
Research Article|
July 01 2022
Three-dimensional measurement of the droplets out of focus in shadowgraphy systems via deep learning-based image-processing method
Special Collection:
Artificial Intelligence in Fluid Mechanics
Zhibo Wang (王治波)
;
Zhibo Wang (王治波)
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University
, Beijing 100084, China
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Feng He (何枫);
Feng He (何枫)
(Funding acquisition, Project administration, Resources, Supervision)
1
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University
, Beijing 100084, China
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Haixiang Zhang (张海翔);
Haixiang Zhang (张海翔)
(Data curation, Funding acquisition, Project administration, Resources, Writing – review & editing)
1
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University
, Beijing 100084, China
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Pengfei Hao (郝鹏飞)
;
Pengfei Hao (郝鹏飞)
(Funding acquisition, Project administration, Resources, Supervision)
1
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University
, Beijing 100084, China
2
AVIC Aerodynamics Research Institute Joint Research Center for Advanced Materials and Anti-Icing, School of Materials Science and Engineering, Tsinghua University
, Beijing 100084, China
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Xiwen Zhang (张锡文);
Xiwen Zhang (张锡文)
(Funding acquisition, Resources, Supervision)
1
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University
, Beijing 100084, China
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Xiangru Li (李庠儒)
Xiangru Li (李庠儒)
a)
(Conceptualization, Supervision, Writing – review & editing)
1
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University
, Beijing 100084, China
a)Author to whom correspondence should be addressed: [email protected]
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a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the special topic, Artificial Intelligence in Fluid Mechanics.
Physics of Fluids 34, 073301 (2022)
Article history
Received:
April 28 2022
Accepted:
June 09 2022
Citation
Zhibo Wang, Feng He, Haixiang Zhang, Pengfei Hao, Xiwen Zhang, Xiangru Li; Three-dimensional measurement of the droplets out of focus in shadowgraphy systems via deep learning-based image-processing method. Physics of Fluids 1 July 2022; 34 (7): 073301. https://doi.org/10.1063/5.0097375
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