In feature-level image fusion, deep learning technology, particularly convolutional sparse representation (SR) theory, has emerged as a new topic over the past three years. This paper proposes an effective image fusion method based on convolution SR, namely, convolutional sparsity-based morphological component analysis and guided filter (CS-MCA-GF). The guided filter operator and choose-max coefficient fusion scheme introduced in this method can effectively eliminate the artifacts generated by the morphological components in the linear fusion, and maintain the pixel saliency of the source images. Experiments show that the proposed method can achieve an excellent performance in multi-modal image fusion, which includes medical image fusion.

中文翻译：

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基于卷积形态成分分析和引导滤波器的多模态图像融合

在特征级图像融合中，深度学习技术，特别是卷积稀疏表示（SR）理论，在过去三年中成为一个新课题。本文提出了一种有效的基于卷积SR的图像融合方法，即基于卷积稀疏的形态分量分析和引导滤波（CS-MCA-GF）。该方法引入的引导滤波算子和choose-max系数融合方案可以有效消除线性融合中形态分量产生的伪影，保持源图像的像素显着性。实验表明，该方法在包括医学图像融合在内的多模态图像融合中取得了优异的性能。