Ms. Wenjun Wang Profile

Wenjun Wang

at Beihang (BUAA)

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 November 2021 Paper

Ground-truth information agnostic deep dehazing network for C919 aircraft image

Wenjun Wang, Yunhao Zhang, Ting-Bing Xu, Zhenzhong Wei

Proceedings Volume 12059, 120591B (2021) https://doi.org/10.1117/12.2612140

KEYWORDS: Image quality, Image processing, Image enhancement, Air contamination, Performance modeling, Image restoration, Visualization, Process modeling, Detection and tracking algorithms, Machine vision

Read Abstract +

Aircraft images captured by a third-party camera during take-off and landing can be used for monitoring and aircraft pose measurement. Hazy weather would severely affect the aircraft image quality and incur the worse visual perception. Haze removal from the aircraft image has become an important task for practical industrial applications. Existing deep learning algorithms need the hazy image and corresponding hazy-free ground-truth image simultaneously for the same scene and time, to learn the dehazing process. However, the ground-truth aircraft images are difficult to obtain, which hinders those approaches from addressing the actual aircraft image dehazing problem. In this paper, we present an endto- end ground-truth information agnostic deep dehazing network for single C919 aircraft image dehazing problem. Instead of the requirement of ground-truth image, we train the network only by utilizing the pair of hazy and predehazed images. The pre-dehazed image can be easily obtained by the conventional dehazing manner without deep learning, and the Natural Image Quality Evaluator (NIQE) is introduced to find the best dehazing model. Compared to existing dehazing algorithms, the proposed algorithm can be capable of addressing real-world hazy C919 aircraft images effectively and achieve the best dehazed performance on our collected aircraft dataset.

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