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Appl. Sci. 2017, 7(2), 161; doi:10.3390/app7020161

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

1
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China
3
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Johannes Kiefer
Received: 18 December 2016 / Revised: 26 January 2017 / Accepted: 28 January 2017 / Published: 9 February 2017
(This article belongs to the Special Issue Optics and Spectroscopy for Fluid Characterization)
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Abstract

In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA) to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP) algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution. View Full-Text
Keywords: Image Fusion; Sparse Representation; Dictionary Construction; Geometric Classification; Multi-focus; Fluorescence Imaging Image Fusion; Sparse Representation; Dictionary Construction; Geometric Classification; Multi-focus; Fluorescence Imaging
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Zhu, Z.; Qi, G.; Chai, Y.; Li, P. A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion. Appl. Sci. 2017, 7, 161.

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