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Proceedings Paper

Transparent object sensing with enhanced prior from deep convolutional neural network
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Paper Abstract

In recent years, with the development of new materials, transparent objects are playing an increasingly important role in many fields, from industrial manufacturing to military technology. However, transparent objects sensing still remains a challenging problem in the area of computational imaging and optical engineering. As an indispensable part of 3-D modeling, transparent object sensing is a long-standing research topic, which aims to reconstruct the surface shape of a given transparent object using various kinds of measurement methods. In this paper, we put forward a new method for the sensing of such objects. Specifically, we focus on the sensing of thin transparent objects, including thin films and various kinds of nano-materials. The proposed method consists of two main steps. Firstly, we use a deep convolutional neural network to predict the original distribution of the objects from its recorded intensity pattern. Secondly, the predicted results are used as initial estimates, and the iterative projection phase retrieval algorithm is performed with the enhanced priors to obtain finer reconstruction results. The numerical experiment results turned out that, with the two steps, our method is able to reconstruct the surface shape of a given thin transparent object with a high speed and simple experimental setup. Moreover, the proposed method shows a new path of transparent object sensing with the combination of state-of-art deep learning technique and conventional computational imaging algorithm. It indicates that, following the same framework, the performance of such method can be significantly improved with more advanced hardware and software implementation.

Paper Details

Date Published: 19 September 2019
PDF: 8 pages
Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 111690H (19 September 2019);
Show Author Affiliations
Jing Wang, Zhejiang Univ. (China)
Jian Bai, Zhejiang Univ. (China)
Xiao Huang, Zhejiang Univ. (China)
Xiangdong Zhou, Zhejiang Univ. (China)
Lei Zhao, Zhejiang Univ. (China)
Kun Yan, Zhejiang Univ. (China)
Jing Hou, China Academy of Engineering Physics (China)
Kailun Yang, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11169:
Artificial Intelligence and Machine Learning in Defense Applications
Judith Dijk, Editor(s)

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