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 blurring 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 WSCG 2000 by Frank Suykens and Yves D. Willems. The idea is to vary with width of the filter inversely proportional to the number of samples available.
As a result, areas with few samples and lots of noise get blurred and smoothed, but areas with lots of samples and low noise are left unaffected. See http://code.google.com/p/flam3/wiki/DensityEstimation.
Not all Flame implementations use density estimation.
Read more about this topic: Fractal Flame
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