BFO-ICA based multi focus image fusion

被引：0

作者：

Agrawal, S. ^{[1
]}

Swain, S. ^{[1
]}

Dora, L. ^{[2
]}

机构：

[1] VSS Univ Technol, Dept Elect & Telecommun Engn, Burla, India

[2] VSS Univ Technol, Dept Elect & Elect Engn, Burla, India

来源：

2013 IEEE SYMPOSIUM ON SWARM INTELLIGENCE (SIS) | 2013年

关键词：

Independent Component Analysis; PSO; BFO;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a pixel based multi focus image fusion technique using independent component analysis (ICA) and bacteria foraging optimization (BFO) algorithm. The basic idea here is to obtain the ICA bases from a set of registered images and optimize them using BFO. The novelty in this paper is that BFO-ICA has not been applied to multi-focus image fusion. The images in the ICA domain are fused and the fused image is then reconstructed using inverse transform. The results are compared with FastICA and PSO-ICA. It is observed that optimizing with BFO yield better result.

引用

页码：194 / 199

页数：6

共 11 条

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