基于孪生卷积神经网络的图像融合

传统的图像融合算法多有计算复杂程度高、不能有效提取图像纹理等不足，为了弥补以上传统算法，提出了一种基于孪生卷积神经网络（Siamese Convolutional Neural Network，Siamese CNN）的图像融合方法.首先，用孪生卷积神经网络生成一个权重图，该权重图包含了来自两个待融合图像的全部像素信息.然后，用图像金字塔对像素以多尺度的方式进行融合，并且采用了局部相似性策略自适应调整分解系数的融合模式.最后，和现存的几种图像融合的方法进行了对比.实验证明，该方法有较好的融合效果，具有一定的可实用性.

Image Fusion Based on Siamese Convolutional Neural Network

Traditional image fusion algorithms have many shortcomings, such as high computational complexity and inability to effectively extract image texture. To compensate these shortcomings of above traditional algorithms, an image fusion method is proposed based on the Siamese Convolutional Neural Network (Siamese CNN). First, we use the Siamese CNN to generate a weight graph, which contains all pixel information from the two images to be fused. Then, the image pyramid is fused in a multi-scale way, and the local similarity strategy is adopted to adjust the decomposition coefficient adaptively. Finally, several existing image fusion methods are compared. Experimental results show that the proposed method has sound fusion effect and is practical to some extent.