Prof. Xingchun Yang Profile

Prof. Xingchun Yang

at Sichuan Police College

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 | 27 November 2019 Paper

Infrared and visible image fusion using joint convolution sparse coding

Chengfang Zhang, Zhen Yue, Dan Yan, Xingchun Yang

Proceedings Volume 11321, 113210V (2019) https://doi.org/10.1117/12.2548445

KEYWORDS: Image fusion, Infrared imaging, Infrared radiation, Visible radiation, Thermography, Convolution, Associative arrays, Image compression, Image enhancement, Image analysis

Read Abstract +

Infrared and visible images possess different types of simultaneous information, but there is a correlation between them. Traditional convolution sparse representation fusion considers the individual characteristics of each image but not the correlation between infrared and visible images. This results in insufficient detail retention and low contrast. To overcome these issues, joint convolution sparse coding is introduced, and a novel visible/infrared image fusion method is proposed. First, low-pass decomposition is used to decompose the source image into low- and high-pass components. Subsequently, joint convolutional sparse coding and a “choose-maximum” fusion strategy are used to fuse base layers, and the "absolute-maximum" is used for detail layers. Finally, image reconstruction is performed on the low and highpass components to obtain a final fused image. The proposed method not only avoids patch-based sparse fusion, which can destroy the image’s global structural features, but also fully integrates related information between infrared and visible images. Four groups of typical infrared and visible images are used for fusion experiments to verify the superiority of the proposed algorithm. The experimental results show that the proposed fusion algorithm provides optimal performance in subjective visual effects and objective evaluation indicators. Compared with the fusion method based on convolution sparse representation, three Q-series objective evaluation indicators increased by 3.83%, 5.31%, and 0.48%, respectively.

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