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 language | English |
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Pages (from-to) | 1-9 |
Number of pages | 9 |
Journal | Neurocomputing |
Volume | 182 |
DOIs | |
Publication status | Published - 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