Existing image fusion methods always use the same representations for different modal medical images. Otherwise, they solve the fusion problem by subjectively defining characteristics to be preserved. However, it leads to the distortion of unique information and restricts the fusion performance. To address the limitations, this paper proposes an unsupervised enhanced medical image fusion network. We perform both surface-level and deep-level constraints for enhanced information preservation. The surface-level constraint is based on the saliency and abundance measurement to preserve the subjectively defined and intuitive characteristics. In the deep-level constraint, the unique information is objectively defined based on the unique channels of a pre-trained encoder. Moreover, in our method, the chrominance information of fusion results is also enhanced. It is because we use the high-quality details in structural images (e.g., MRI) to alleviate the mosaic in functional images (e.g., PET, SPECT). Both qualitative and quantitative experiments demonstrate the superiority of our method over the state-of-the-art fusion methods.

现有的图像融合方法总是对不同的模态医学图像使用相同的表示。否则，他们通过主观定义要保留的特征来解决融合问题。然而，它导致独特信息的失真并限制了融合性能。为了解决这些局限性，本文提出了一种无监督的增强医学图像融合网络。我们执行表面级和深层约束以增强信息保存。表面级约束基于显着性和丰度测量，以保留主观定义和直观的特征。在深层约束中，唯一信息是基于预训练编码器的唯一通道客观定义的。此外，在我们的方法中，融合结果的色度信息也得到了增强。这是因为我们使用了结构图像中的高质量细节（例如，MRI）以减轻功能图像（例如，PET，SPECT）中的马赛克。定性和定量实验都证明了我们的方法优于最先进的融合方法。