Multi-objective optimization and gray association for multi-focus image fusion. Issue 2 (June 2016)

• Record Type: Journal Article
• Authors: Wang, Suli; Luo, Xiaoqing
• Is Part Of: Journal of algorithms & computational technology. Volume 10:Issue 2(2016)
• Page Start: 90
• Page End: 98
• Publication Date: 2016-06
• Subjects: Vector-evaluated quantum-behaved particle swarm optimization -- gray relational analysis -- region -- entropy component analysis -- multi-focus image fusion; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301816640712
• Languages: English
• ISSNs: 1748-3018

Abstract

Multi-objective optimization and gray association are classical technologies in intelligent system. In this paper, a novel multi-focus image fusion method based on the technology is proposed. First, the input images are decomposed into low-frequency subband and high-frequency subbands using shift invariant Shearlet transform. Second, low-frequency subbands are fused by the weighted average fusion rule. The weight can be adaptively obtained by vector-evaluated quantum-behaved particle swarm optimization and gray relation analysis. The similarity of a corresponding region-based fusion rule is proposed and used to integrate high-frequency subbands. Last but not least, fusion image is obtained by inverse SIST. Visual and statistical analyses demonstrate that the fusion quality can be significantly improved by the proposed method.