Editing Fractal flame (section)

==Density estimation==

The flame algorithm is like a [[Monte Carlo simulation]], with the flame quality directly proportional to the number of iterations of the simulation. The noise that results from this stochastic sampling can be reduced by [[Gaussian blur|blur]]ring the image, to get a smoother result in less time. One does not however want to lose resolution in the parts of the image that receive many samples and so have little noise.

This problem can be solved with adaptive [[density estimation]] to increase image quality while keeping render times to a minimum.  FLAM3 uses a simplification of the methods presented in *Adaptive Filtering for Progressive Monte Carlo Image Rendering*, a paper presented at [[Winter School of Computer Graphics|WSCG]] 2000 by Frank Suykens and Yves D. Willems. The idea is to vary the width of the filter [[inversely proportional]] to the number of samples available.

As a result, areas with few samples and high noise become blurred and smoothed, but areas with many samples and low noise are left unaffected.<ref>See https://github.com/scottdraves/flam3/wiki/Density-Estimation.</ref>

Not all Flame implementations use density estimation.