Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion

Biting Yu, Bo Jia, Lu Ding, Zhengxiang Cai, Qi Wu, Chun Hung Roberts Law, Jiayang Huang, Lei Song, Shan Fu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

51 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalNeurocomputing
Volume182
DOIs
Publication statusPublished - 19 Mar 2016

Keywords

  • Bacterial foraging optimization
  • Dual-tree complex wavelet transform
  • Multi-focus image fusion
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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