A Collection of Image Smoothing Algorithms
Image Smoothing via L0 Gradient Minimization
S = L0Smoothing(Im, lambda, kappa)
Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid
R = lapfilter(I,sigma_r,alpha,beta,colorRemapping,domain)
Edge-Preserving Decompositions for Multi-Scale Tone and Detail Manipulation
A Fast Approximation of the Bilateral Filter using a Signal Processing Approach
B = bfilter2(A,w,sigma)
Nonlinear total variation based noise removal algorithms
out = SplitBregmanROF(image,mu,tol)
Fast Global Image Smoothing Based on Weighted Least Squares
F = FGS(img, sigma, lambda, joint_image, num_iterations, attenuation)
Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree
OUT = TreeFilterRGB_Uint8(uint8_rgbimg,sigma,sig_s[,sig_r=0.05[,num_iter=1]])
Edge-Avoiding Wavelets and their Applications
[A W] = EAW(I, nlevels, wavelet_type, dist_func, sigma)
Diffusion Maps for Edge-Aware Image Editing
Additional
Rolling Guidance Filter: http://www.cse.cuhk.edu.hk/leojia/projects/rollguidance/ Guided Image Filtering: http://kaiminghe.com/eccv10/ Adaptive Manifolds: http://inf.ufrgs.br/~eslgastal/AdaptiveManifolds/ https://arxiv.org/abs/1503.07297v1 P Milanfar: A tour of modern image filtering: New insights and methods, both practical and theoretical, 2013 google scholar: https://goo.gl/FFU61H pdf: https://www.researchgate.net/profile/Peyman_Milanfar2/publication/258792315_A_Tour_of_Modern_Image_Filtering_New_Insights_and_Methods_Both_Practical_and_Theoretical/links/5465578c0cf2052b509f2f61/A-Tour-of-Modern-Image-Filtering-New-Insights-and-Methods-Both-Practical-and-Theoretical.pdf