Adaptive structured dictionary learning for image fusion based on group-sparse-representation

Jiajie Yang; Bin Sun; Chengwei Luo; Yuzhong Wu; Limei Xu

doi:10.1117/12.2304585

10 April 2018 Adaptive structured dictionary learning for image fusion based on group-sparse-representation

Jiajie Yang, Bin Sun, Chengwei Luo, Yuzhong Wu, Limei Xu

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061535 (2018) https://doi.org/10.1117/12.2304585
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a ℓ1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Citation Download Citation

Jiajie Yang, Bin Sun, Chengwei Luo, Yuzhong Wu, and Limei Xu "Adaptive structured dictionary learning for image fusion based on group-sparse-representation", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061535 (10 April 2018); https://doi.org/10.1117/12.2304585

ACCESS THE FULL ARTICLE

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image fusion

Associative arrays

Visualization

Chemical species

Infrared imaging

Infrared radiation

Medical imaging

Show All Keywords

Keywords/Phrases

Search In:

Publication Years