Infrared and visible image fusion is a hot research direction in the fields of computer vision and image processing, and it is a common multimodal image fusion. An effective image fusion algorithm via convolutional sparse representation (CSR) and guided filtering is proposed for fusing infrared and visible images in this paper. First, a series of dictionary filters are trained by the CSR strategy, and the smooth image component and detailed image component are obtained by classifying those filters into high-pass filters and low-pass filters. Then two rules are designed to fuse the smooth image component and detailed image component, respectively. For the detailed image component, a weight construction method based on guided filtering is designed to get the weight maps, and the smooth image component is fused by applying the “choose-max” strategy to the corresponding sparse coefficients. Finally, the fused image is obtained by combining the fused smooth image component and detailed image component. Experimental results show that the proposed algorithm achieves good fusion results and exhibits advantages over comparison image fusion methods.

中文翻译：

---

基于卷积稀疏表示和引导滤波的红外和可见光图像融合

红外与可见光图像融合是计算机视觉和图像处理领域的一个热门研究方向，是一种常见的多模态图像融合。本文提出了一种通过卷积稀疏表示（CSR）和引导滤波的有效图像融合算法来融合红外和可见光图像。首先，通过CSR策略训练一系列字典滤波器，通过将这些滤波器分类为高通滤波器和低通滤波器来获得平滑图像分量和详细图像分量。然后设计了两条规则来分别融合平滑图像分量和细节图像分量。对于细节图像分量，设计了一种基于引导滤波的权重构建方法，得到权重图，通过对相应的稀疏系数应用“choose-max”策略来融合平滑图像分量。最后将融合后的平滑图像分量和细节图像分量相结合得到融合图像。实验结果表明，该算法取得了良好的融合效果，并优于对比图像融合方法。