Prof. Haiwen Yuan Profile

Prof. Haiwen Yuan

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 | 13 June 2024 Paper

Adaptive highlight removal through generative adversarial networks and color adjustment

Shengrui Jiang, Li Cheng, Haiwen Yuan, Xuan Li

Proceedings Volume 13180, 131806S (2024) https://doi.org/10.1117/12.3033742

KEYWORDS: Image processing, Image restoration, Image fusion, Image enhancement, Education and training, Specular reflections, Reflection, Light sources and illumination, Matrices, Gallium nitride

Read Abstract +

The detection and removal of highlights are fundamental issues in computer vision and image processing. However, existing methods for highlight removal have certain drawbacks. For example, many existing methods are not suitable for repairing large highlight areas, and the applicability of current methods is relatively limited, requiring re-adjustment of parameters in different usage scenarios. Therefore, this paper proposes an effective deep learning model that can address highlight issues in a wide range of scenarios, specifically designed for removing highlights in individual images. The proposed approach is to first detect the positions of highlights and generate a highlight mask. The training process consists of two main parts. Initially, the highlight removal network is trained. This network, based on a generative adversarial network, generates information about the portions of the image affected by highlights. However, there may still be color deviations in the original highlight positions after generation. To address this, the desthighlighted image and the highlight mask are fed into a color adjustment network for secondary training. This corrects the colors in the repaired area, ultimately producing the final image after highlight removal.

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