Adaptive Sampling Based on Fuzzy Uncertainty

An adaptive sampling technique to calculate the global illumination with Monte Carlo path tracing is presented.It takes different number of sampling for different pixel to enhance the indirect illumination and to reduce image noise.Based on the intrinsic fuzzy uncertainty in image noise estimation,we define a fuzzy set based on the initial sampling results for each pixel and propose a new noise metric by exploiting the idea of fuzziness defined in fuzzy set theory.With the proposed noise metric,we can perform efficient adaptive sampling to determine whether super sampling is needed or not for each pixel.Extensive experiment results show that our novel method can achieve significantly better results than presently existing algorithms.