Next Article in Journal
Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy
Next Article in Special Issue
The Expected Missing Mass under an Entropy Constraint
Previous Article in Journal
Node Importance Ranking of Complex Networks with Entropy Variation
Previous Article in Special Issue
Face Verification with Multi-Task and Multi-Scale Feature Fusion
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(7), 306;

A Novel Geometric Dictionary Construction Approach for Sparse Representation Based Image Fusion

3,5,* and 5
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China
Author to whom correspondence should be addressed.
Received: 17 April 2017 / Revised: 21 June 2017 / Accepted: 26 June 2017 / Published: 27 June 2017
(This article belongs to the Special Issue Information Theory in Machine Learning and Data Science)
View Full-Text   |   Download PDF [4272 KB, uploaded 28 June 2017]   |  


Sparse-representation based approaches have been integrated into image fusion methods in the past few years and show great performance in image fusion. Training an informative and compact dictionary is a key step for a sparsity-based image fusion method. However, it is difficult to balance “informative” and “compact”. In order to obtain sufficient information for sparse representation in dictionary construction, this paper classifies image patches from source images into different groups based on morphological similarities. Stochastic coordinate coding (SCC) is used to extract corresponding image-patch information for dictionary construction. According to the constructed dictionary, image patches of source images are converted to sparse coefficients by the simultaneous orthogonal matching pursuit (SOMP) algorithm. At last, the sparse coefficients are fused by the Max-L1 fusion rule and inverted to a fused image. The comparison experimentations are simulated to evaluate the fused image in image features, information, structure similarity, and visual perception. The results confirm the feasibility and effectiveness of the proposed image fusion solution. View Full-Text
Keywords: image fusion; sparse representation; dictionary construction; geometric classification image fusion; sparse representation; dictionary construction; geometric classification

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, K.; Qi, G.; Zhu, Z.; Chai, Y. A Novel Geometric Dictionary Construction Approach for Sparse Representation Based Image Fusion. Entropy 2017, 19, 306.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top