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Optical Engineering

Dictionary learning method for joint sparse representation-based image fusion
Author(s): Qiheng Zhang; Yuli Fu; Haifeng Li; Jian Zou
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Paper Abstract

Recently, sparse representation (SR) and joint sparse representation (JSR) have attracted a lot of interest in image fusion. The SR models signals by sparse linear combinations of prototype signal atoms that make a dictionary. The JSR indicates that different signals from the various sensors of the same scene form an ensemble. These signals have a common sparse component and each individual signal owns an innovation sparse component. The JSR offers lower computational complexity compared with SR. First, for JSR-based image fusion, we give a new fusion rule. Then, motivated by the method of optimal directions (MOD), for JSR, we propose a novel dictionary learning method (MODJSR) whose dictionary updating procedure is derived by employing the JSR structure one time with singular value decomposition (SVD). MODJSR has lower complexity than the K-SVD algorithm which is often used in previous JSR-based fusion algorithms. To capture the image details more efficiently, we proposed the generalized JSR in which the signals ensemble depends on two dictionaries. MODJSR is extended to MODGJSR in this case. MODJSR/MODGJSR can simultaneously carry out dictionary learning, denoising, and fusion of noisy source images. Some experiments are given to demonstrate the validity of the MODJSR/MODGJSR for image fusion.

Paper Details

Date Published: 22 May 2013
PDF: 12 pages
Opt. Eng. 52(5) 057006 doi: 10.1117/1.OE.52.5.057006
Published in: Optical Engineering Volume 52, Issue 5
Show Author Affiliations
Qiheng Zhang, South China Univ. of Technology (China)
Yuli Fu, South China Univ. of Technology (China)
Haifeng Li, South China Univ. of Technology (China)
Jian Zou, South China Univ. of Technology (China)

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