Anisotropic diffusion for Monte Carlo noise reduction
Monte Carlo sampling can be used to estimate solutions to global light transport and other rendering problems. However, a large number of observations may be needed to reduce the variance to acceptable levels. Rather than computing more observations within each pixel, if spatial coherence exists in image space it can be used to reduce visual error by averaging estimators in adjacent pixels. Anisotropic diffusion is a space-variant noise reduction technique that can selectively preserve texture, edges, and other details using a map of image coherence. The coherence map can be estimated from depth and normal information as well as interpixel color distances. Incremental estimation of the reduction in variance, in conjunction with statistical normalization of interpixel color distances, yields an energy-presenting algorithm that converges to a spatially nonconstant steady state.
anisotropic diffusion noise reduction light transport global illumination image processing image synthesis monte carlo methods space-variant filtering