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Adaptive Filtering for Progressive Monte Carlo Image Rendering

Image filtering is often applied as a post-process to Monte Carlo generated pictures, in order to reduce 
noise. In this paper we present an algorithm based on density estimation techniques that applies an energy 
preserving adaptive kernel filter to individual samples during image rendering. The used kernel widths 
diminish as the number of samples goes up, ensuring a reasonable noise versus bias trade-off at any time. 
This results in a progressive algorithm, that still converges asymptotically to a correct solution. Results 
show that general noise as well as spike noise can effectively be reduced. Many interesting extensions are 
possible, making this a very promising technique for Monte Carlo image synthesis.