This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.

中文翻译：

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卷积稀疏性PCNN的基于形态成分分析的感知医学图像融合

本文提出了一种基于形态学成分分析，结合卷积稀疏性和脉冲耦合神经网络的感知医学图像融合框架，简称MCA-CS-PCNN。首先通过形态成分分析将源图像分解为卡通成分和纹理成分，然后通过预先学习的字典生成卡通层和纹理层的卷积稀疏表示。然后，将卷积稀疏性用作刺激，以激励PCNN处理卡通层和纹理层。最后，通过融合融合的卡通层和纹理层来计算医学融合图像。实验结果证明，MCA-CS-PCNN模型优于最新的融合策略。