Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12540/259
Title: Grey wolf optimiser-based feature selection for feature-level multi-focus image fusion
Authors: Krishnamoorthy, Sujatha 
D. Shalini, Punithavathani 
J. Janet 
S. Venkatalakshmi 
Issue Date: 2020
Publisher: Inderscience Publishers
Source: Sujatha, K., Punithavathani, D. S., Janet, J., & Venkatalakshmi, S. (2020). Grey wolf optimiser-based feature selection for feature-level multi-focus image fusion. International Journal of Business Intelligence and Data Mining, 16(3), 279–279.
Journal: International Journal of Business Intelligence and Data Mining 
Abstract: This paper proposes optimal ensemble-individual-features (OEIF) for multi-focus image fusion through combining the decision information of individual features. This proposed system consists of three stages. In the first stage, the different types of features such as spatial, texture and frequency are extracted from every block on input blurred images. In the second step, grey wolf optimiser (GWO)-based features validation method is proposed to find suitable features from source images. This method is based on an iterative process, in which each individual represents a candidate solution for validating/invalidating the features. In the final step, the ensemble decision based on optimal individual features is utilised to fuse blurred images. We prove that OEIF method is better in comparison to the noisy feature-based individual pixel-level and the feature-level fusion methods with different multi-focus images and it reveals that OGWO-based proposed method performs better visual quality than other methods.
Description: Please note that preprint copy is not available on WIRE. Please contact wire@wku.edu.cn to request an electronic copy of this item.
URI: https://hdl.handle.net/20.500.12540/259
DOI: 10.1504/IJBIDM.2020.106140
Appears in Collections:Scholarly Publications

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