Dr. Yuan Gao Profile

Dr. Yuan Gao

at Shanghai Univ.

SPIE Involvement:

Author

Publications (2)

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 | 8 December 2023 Paper

An unsupervised fusion method for infrared and visible image fusion under low-light condition

Shuai Yang, Yuan Gao, Shiwei Ma, Kaihua Huang

Proceedings Volume 12943, 129430O (2023) https://doi.org/10.1117/12.3014572

Read Abstract +

The aim of fusing infrared and visible images is to achieve high-quality images by enhancing textural details and obtaining complementary benefits. Since the details of the visible images are not obvious in low light, it is difficult for the current fusion methods to complete the complementary contours and texture details. With the intention of addressing the challenge of poor quality of infrared and visible light fusion images under low light conditions, a novel fusion method for infrared and visible light is presented in this study utilizing generative adversarial networks (referred to as UFIVL). Specifically, based on the existing densely connected decoder, pruning is introduced to reduce the network complexity without quality loss. A new overall optimization objective includes the adaptive limit contrast histogram equalization loss and the joint gradient loss are designed to deal with the defects of high contrast and brightness loss of the fused image, and the difficulty of capturing detailed features in low light scenes, respectively. Experimental results on LLVIP datasets show that compared with other state-of-the-art methods, the fused image generated by the proposed method has better subjective and objective performances.

Proceedings Article | 11 November 2021 Paper

UD-GAN: a dense connected generative adversarial network for pixel-level modal translation of multimodal images

Yuan Gao, Shiwei Ma

Proceedings Volume 12076, 1207603 (2021) https://doi.org/10.1117/12.2607172

KEYWORDS: Deconvolution, Image quality, Convolution

Read Abstract +

Modal translation between multimodal images is an effective complementary scheme when images with some certain modal are difficult to obtain. Since pixel level image modal translation method can obtain the images with the highest quality, it has become a research hotspot in recent years. Generative adversarial network (GAN) is a network for image generation, due to the complex structure of GAN and the complexity of the image generation task, the training results of GAN are not stable. In this paper, on the basis of U-nets, the dense block is used to increase the feature information in the subsampling encoding and up-sampling decoding operation, so as to reduce the loss of information and obtain higher quality images. At the same time, the dense long connection is introduced to connect the encoding and decoding operations of the same stage, so that the network can effectively combine the features at low and high level, and improve the performance of the network. Experimental results show that the proposed method is effective in modal translation of multimodal images, and the image quality is better than some state-of-the-art methods.

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