Li Yang, Yang Haitao, Gao Yuge
Center for Space Security Studies, University of Aerospace Engineering, Beijing, China.
Emerg Med Int. 2022 Aug 25;2022:1693531. doi: 10.1155/2022/1693531. eCollection 2022.
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.
为了解决融合图像中的伪影问题以及现有融合算法在不同场景下泛化能力不足的问题，本文提出了一种基于改进的RGF和视觉显著性图的图像融合算法，以实现红外与可见光图像以及多模态医学图像的融合。首先，本文使用RGF（滚动引导滤波器）和高斯滤波器按不同尺度将图像分解为基底层、中间层和细节层。其次，通过对源图像的计算获得视觉权重图，并使用引导滤波器更好地指导基底层融合。然后，通过最大局部方差实现中间层融合，通过像素的最大绝对值实现细节层融合。最后，通过权重融合获得融合图像。实验表明，与对比方法相比，该方法具有更好的综合性能，在红外与可见光图像以及医学图像的融合中取得了更好的效果。
