邊緣保留濾波器（圖像平滑方法）
1.多尺度邊緣保留與分解
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.均值濾波
S. Li, X. Kang, J. Hu, Image fusion with guided filtering, IEEE Trans. Image
Process. 22 (7) (2013) 2864–2875.（引導濾波用的均值濾波進行的分解）
代碼：https://www.mathworks.com/matlabcentral/fileexchange/68962-a-demo-for-image-fusion
3.非局部均值濾波
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.交叉雙邊濾波
B. K. S. Kumar, “Image fusion based on pixel significance using cross
bilateral filter,” Signal Image Video Processing, 1–12 (2013)
代碼：https://www.mathworks.com/matlabcentral/fileexchange/43781-image-fusion-based-on-pixel-significance-using-cross-bilateral-filter
（和GFS相似）
5.引導濾波+統計像素方法
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.
代碼：https://www.mathworks.com/matlabcentral/fileexchange/64529-fusion-of-mri-and-ct-images-using-guided-image-filter-and-image-statistics
6.各向異性擴散
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.
代碼：https://www.mathworks.com/matlabcentral/fileexchange/63591-fusion-of-infrared-and-visible-sensor-images-based-on-anisotropic-diffusion-and-kl-transform
1.用各項異性模糊得到基礎層和紋理層
2.使用線性加權得到融合後的基礎層
3.用KL變換得到細節層
4.疊加最後的細節層和紋理層
7.四階偏微分方程
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.
代碼：https://www.mathworks.com/matlabcentral/fileexchange/63570-multi-sensor-image-fusion-based-on-fourth-order-partial-differential-equations
8.滾動引導濾波和高斯濾波
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.
代碼：https://github.com/hli1221/imagefusion_Infrared_visible_latlrr
細節層融合：
（1）求絕對值最大權重圖
（2）高斯濾波權重圖
（2）通過最小二乘法最小化
融合-濾波 和 融合-可見光 以及通過紅外權重控制
