To solve the artifact problem in fused images and the lack of enough generalization under different scenarios of existing fusion algorithms, the paper proposes an image fusion algorithm based on improved RGF and visual saliency map to realize fusion for infrared and visible light images and a multimode medical image. Firstly, the paper uses RGF (rolling guidance filter) and Gaussian filter to decompose the image into the base layer, interlayer, and detail layer by a different scale. Secondly, the paper obtains a visual weight map by the calculation of the source image and uses the guided filter to better guide the base layer fusion. Then, it realizes the interlayer fusion through maximum local variance and realizes the detail layer fusion through the maximum absolute value of the pixel. Finally, it obtains the fused image through weight fusion. The experiment demonstrates that the proposed method shows better comprehensive performance and obtains better results in fusion for infrared and visible light images and medical images compared to the contrast method.

中文翻译：

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一种基于改进RGF和视觉显着图的图像融合算法


针对现有融合算法在不同场景下融合图像存在伪影问题以及缺乏足够的泛化能力，提出一种基于改进RGF和视觉显着图的图像融合算法，实现红外与可见光图像的融合以及多模式医学图像融合。图像。首先，本文使用RGF（滚动引导滤波器）和高斯滤波器将图像按不同尺度分解为基础层、中间层和细节层。其次，论文通过源图像的计算得到视觉权重图，并使用引导滤波器更好地引导基础层融合。然后，通过最大局部方差实现层间融合，通过像素最大绝对值实现细节层融合。最后通过权值融合得到融合图像。实验表明，与对比方法相比，该方法在红外、可见光图像和医学图像的融合方面表现出更好的综合性能，取得了更好的效果。