Masses of research decompose the image into different levels of feature maps, but the structures and edges may not appropriately separated. This may cause the loss of image detail in the fusion process. Therefore, we design a robust method for multimodal medical image fusion using spectral total variation transform (STVT). In our method, the source images are first decomposed into a series of texture signatures (referred to as deviation components) and base components via STVT algorithm. Then combine the local structural patch measurement (LSPM) to fuse the deviation components, and the base components are merged using a spatial frequency (SF) dual‐channel spiking cortical model (SF‐DCSCM), in which the SF of base components are regarded as stimulus to activate DCSCM. Finally, the final image is reconstructed by the inverse STVT with the restored images together. Experimental results suggest that proposed scheme achieves promising results, and more competitiveness against some state‐of‐the‐art methods.

中文翻译：

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基于光谱总变化和局部结构斑块测量的多峰医学图像融合

大量研究将图像分解为不同级别的特征图，但结构和边缘可能未适当分离。这可能会导致融合过程中图像细节的丢失。因此，我们设计了一种可靠的方法，可以使用光谱总变化变换（STVT）进行多峰医学图像融合。在我们的方法中，首先通过STVT算法将源图像分解为一系列纹理特征（称为偏差分量）和基础分量。然后结合局部结构补丁测量（LSPM）融合偏差成分，并使用空间频率（SF）双通道峰值皮质模型（SF-DCSCM）合并基本成分，其中考虑了基本成分的SF作为刺激来激活DCSCM。最后，最终的图像由逆STVT与恢复的图像一起重建。实验结果表明，提出的方案取得了可喜的结果，并且与某些最新方法相比更具竞争力。