Image fusion technique is an effective way to merge the information contained in different imaging modalities by generating a more informative composite image. Fusion of green fluorescent protein (GFP) and phase contrast images is of great significance to the subcellular localization, the functional analysis of protein, and the expression of gene. In this article, a phase congruency (PC)‐based GFP and phase contrast image fusion method in nonsubsampled shearlet transform (NSST) domain is presented. The input images are decomposed by the NSST to acquire the multiscale and multidirection representations. The high‐frequency coefficients are fused with a strategy based on PC and parameter‐adaptive pulse coupled neural network (PA‐PCNN), while the low‐frequency coefficients are integrated through a local energy (LE)‐based rule. Finally, the fused image is generated by conducting the inverse NSST on the merged high‐ and low‐frequency coefficients. Experimental results illustrate that the presented method outperforms several state‐of‐the‐art GFP and phase contrast image fusion algorithms on both qualitative and quantitative assessments.

中文翻译：

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非下采样剪切波变换域中基于相位一致性的绿色荧光蛋白与相衬图像融合方法。

图像融合技术是一种通过生成信息量更大的合成图像来融合不同成像模式中包含的信息的有效方法。绿色荧光蛋白（GFP）与相差图像的融合对亚细胞定位、蛋白质功能分析和基因表达具有重要意义。在本文中，提出了一种在非下采样剪切波变换 (NSST) 域中基于相位一致性 (PC) 的 GFP 和相衬图像融合方法。输入图像由 NSST 分解以获得多尺度和多方向表示。高频系数采用基于 PC 和参数自适应脉冲耦合神经网络 (PA-PCNN) 的策略进行融合，而低频系数则通过基于局部能量 (LE) 的规则进行积分。最后，融合图像是通过对合并的高频和低频系数进行逆 NSST 生成的。实验结果表明，所提出的方法在定性和定量评估方面都优于几种最先进的 GFP 和相衬图像融合算法。