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Entropy 2017, 19(7), 306; doi:10.3390/e19070306

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

1,2,5
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3,4
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3,5,* and 5
1
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
2
Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, China
3
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
5
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]   |  

Abstract

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

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