关键词: 非下采样剪切波变换（NSST）, 图像融合, GA优化PCNN, 融合规则, 评价指标

Abstract: Aiming at traditional multi-scale fusion algorithm without translation, the fusion effect is poorer, and the parameters setting of Pulse Coupled Neural Network（PCNN） is complex and so on, this paper proposes an image fusion method using the combination NonSubsampled Shearlets Transform（NSST） and Genetic Algorithm（GA） to optimize the parameters of the PCNN, the maximum values of fusion indicators（mutual information [MI], edge information reservation [QAB/F], entropy [EN], spatial frequency [SF], Standard Deviation [STD] and Average Gradient [AG]） are set as the objective function of the GA optimization algorithm to obtain the optimal parameters solution of PCNN, such as linking strength, threshold and so on. Firstly, the source images are decomposed via NSST; then low frequency subband and high frequency subband are fused with Sum-Modified-Laplacian（SML） and PCNN which is guided by Space Frequency（SF）, respectively; finally NSST inverse transformation is used to get the final fusion image. According to the subjective evaluation and objective evaluation indicators, the fused effects of multi-focus image, medical image and infrared and visible light image are carried through the evaluation analysis. The experimental results show that the algorithm objective evaluation indicators are superior to other algorithms, have better fusion effect.

Key words: NonSubsampled Shearlets Transform（NSST）, image fusion, GA optimize PCNN, fusion rules, evaluation indicators