边缘保留滤波器（图像平滑方法）
1.多尺度边缘保留与分解
Z. Farbman, R. Fattal, D. Lischinski, Edge-preserving decompositions for multi-scale tone and detail manipulation, ACM Trans. Graph. 27 (3) (2008) 1–10.
2.均值滤波
S. Li, X. Kang, J. Hu, Image fusion with guided filtering, IEEE Trans. Image
Process. 22 (7) (2013) 2864–2875.（引导滤波用的均值滤波进行的分解）
代码：https://www.mathworks.com/matlabcentral/fileexchange/68962-a-demo-for-image-fusion
3.非局部均值滤波
X. Yan, H. Qin, J. Li, H. Zhou, J.-g. Zong, Q. Zeng, Infrared and visible image
fusion using multiscale directional nonlocal means filter, Appl. Opt. 54 (13)
(2015) 4299–4308.
4.交叉双边滤波
B. K. S. Kumar, “Image fusion based on pixel significance using cross
bilateral filter,” Signal Image Video Processing, 1–12 (2013)
代码：https://www.mathworks.com/matlabcentral/fileexchange/43781-image-fusion-based-on-pixel-significance-using-cross-bilateral-filter
（和GFS相似）
5.引导滤波+统计像素方法
Bavirisetti D P, Kollu V, Gang X, et al. Fusion of MRI and CT images using guided image filter and image statistics[J]. International Journal of Imaging Systems and Technology, 2017, 27(3): 227-237.
代码：https://www.mathworks.com/matlabcentral/fileexchange/64529-fusion-of-mri-and-ct-images-using-guided-image-filter-and-image-statistics
6.各向异性扩散
Bavirisetti D P, Dhuli R. Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform[J]. IEEE Sensors Journal, 2015, 16(1): 203-209.
代码：https://www.mathworks.com/matlabcentral/fileexchange/63591-fusion-of-infrared-and-visible-sensor-images-based-on-anisotropic-diffusion-and-kl-transform
1.用各项异性模糊得到基础层和纹理层
2.使用线性加权得到融合后的基础层
3.用KL变换得到细节层
4.叠加最后的细节层和纹理层
7.四阶偏微分方程
Bavirisetti D P, Xiao G, Liu G. Multi-sensor image fusion based on fourth order partial differential equations[C]//2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017: 1-9.
代码：https://www.mathworks.com/matlabcentral/fileexchange/63570-multi-sensor-image-fusion-based-on-fourth-order-partial-differential-equations
8.滚动引导滤波和高斯滤波
Ma J, Zhou Z, Wang B, et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 2017, 82: 8-17.
代码：https://github.com/hli1221/imagefusion_Infrared_visible_latlrr
细节层融合：
（1）求绝对值最大权重图
（2）高斯滤波权重图
（2）通过最小二乘法最小化
融合-滤波 和 融合-可见光 以及通过红外权重控制
来源：CSDN
作者：ZHANGWENJUAN1995
链接：https://blog.csdn.net/ZHANGWENJUAN1995/article/details/90515279