Robust Statistical Pixel Estimation

Abstract

Robust statistical methods are employed to reduce the noise in Monte Carlo ray tracing. Through the use of resampling, the sample mean distribution is determined for each pixel. Because this distribution is uni-modal and normal for a large sample size, robust estimates converge to the true mean of the pixel values. Compared to existing methods, less additional storage is required at each pixel because the sample mean distribution can be distilled down to a compact size, and fewer computations are necessary because the robust estimation process is sampling independent and needs a small input size to compute pixel values. The robust statistical pixel estimators are not only resistant to impulse noise, but they also remove general noise from fat-tailed distributions. A substantial speedup in rendering can therefore be achieved by reducing the number of samples required for a desired image quality. The effectiveness of the proposed approach is demonstrated for path tracing simulations.