Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62374
Title: Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion
Authors: Yu, B
Jia, B
Ding, L
Cai, Z
Wu, Q
Law, R 
Huang, J
Song, L
Fu, S
Keywords: Multi-focus image fusion
Dual-tree complex wavelet transform
Support vector machine
Bacterial foraging optimization
Issue Date: 2016
Publisher: Elsevier
Source: Neurocomputing, 2016, v. 182, p. 1-9 How to cite?
Journal: Neurocomputing 
Abstract: This study proposed a new method for multi-focus image fusion using hybrid wavelet and classifier. The image fusion process was formulated as a two-class classification problem: in and out-of-focus classes. First, a six-dimensional feature vector was extracted using sub-bands of dual-tree complex wavelet transform (DT-CWT) coefficients from the source images, which were then projected by a trained two class support vector machine (SVM) to the class labels. A bacterial foraging optimization algorithm (BFOA) was developed to obtain the optimal parameters of the SVM. The output of the classification system was used as a decision matrix for fusing high-frequency wavelet coefficients from multi-focus source images in different directions and decomposition levels of the DT-CWT. After the high and low frequency coefficients of the source images were fused, the final fused image was obtained using the inverse DT-CWT. Several existing methods were compared with the proposed method. Experimental results showed that our presented method outperformed the existing methods, in visual effect and in objective evaluation.
URI: http://hdl.handle.net/10397/62374
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2015.10.084
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