Adaptive Sampling Guided by SURE and Sample Color Histogram Reconstruction

We present an approach for computing pixel-wise mean squared error based on Stein’s unbiased risk estimator（SURE）, to perform adaptive sampling for histogram-based sample reconstruction in Monte Carlo path tracing. Firstly, the image space is coarsely sampled; then we compute SURE on GPU by block estimates error metric of each pixel, to guide subsequent non-uniform adaptive sampling. Finally, nonlocal multi-scale filtering is used to reconstruct image. The experimental results demonstrate substantial improvements in terms of both numerical error and visual quality.