Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as Algorithm Unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i.e., separating low-frequency base information and high-frequency detail information from source images. Then the algorithm unrolling is implemented where each iteration is mapped to a CNN layer and each optimization model is transformed into a trainable neural network. Compared with the general network architectures, the proposed framework combines the model-based prior information and is designed more reasonably. After the unrolling operation, our model contains two decomposers (encoders) and an additional reconstructor (decoder). In the training phase, this network is trained to reconstruct the input image. While in the test phase, the base (or detail) decomposed feature maps of infrared/visible images are merged respectively by an extra fusion layer, and then the decoder outputs the fusion image. Qualitative and quantitative comparisons demonstrate the superiority of our model, which can robustly generate fusion images containing highlight targets and legible details, exceeding the state-of-the-art methods. Furthermore, our network has fewer weights and faster speed.

翻译：红外和可见图像聚合(IVIF) 期望从可见图像中获取保留红红外图像和纹理细节的热辐射信息的图像。 在本文中, 提议采用模型化的神经神经网络(CNN)模型, 称为Alogorithm Unrolling 图像融合(AUIF), 以克服传统CNN的图像融合(AUIF) 模型的缺点。 拟议的AUIF模型以两种传统优化模型的迭接公式为起点, 这两种模型的迭接公式是为了完成双级分解, 即将低频基础信息与源图像的高频详细信息分隔开来。 在培训阶段, 这个网络的算法是, 将每个循环图绘制成CNN层, 将每个优化模型转换成可训练的神经神经网络 。 与一般网络结构相比, 拟议的框架将基于模型的先前信息并设计得更合理。 在分解操作后, 我们的模型包含两个解析的直径直径直径直径直径直径直径直径直径直径直径直径直的直径直径直径直径直径直径直径直、 和分解( decol) 。 。 这个网络的图像图的图解的比再解的图解的图解的模型的模型的解的模型的模型的模型的模型的模型的模型的模型的模型和再分解,, 。