Multi-objective optimization and gray association for multi-focus image fusion. Issue 2 (June 2016) - Wang, Suli; Luo, Xiaoqing
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.