Mr. Kun Yan Profile

Kun Yan

at Zhejiang Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 19 September 2019 Presentation + Paper

Transparent object sensing with enhanced prior from deep convolutional neural network

Jing Wang, Jian Bai, Xiao Huang, Xiangdong Zhou, Lei Zhao, Kun Yan, Jing Hou, Kailun Yang

Proceedings Volume 11169, 111690H (2019) https://doi.org/10.1117/12.2533173

KEYWORDS: Phase retrieval, Convolutional neural networks, Neural networks, Reconstruction algorithms, Evolutionary algorithms, Computational imaging, Sensing systems, Image processing, Imaging systems, 3D surface sensing

Read 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.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years