SURE-based Optimization for Adaptive Sampling and Reconstruction
SURE-based Optimization for Adaptive Sampling and Reconstruction
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DOI：
10.1145/2366145.2366213
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发表时间：
2012-11-01
影响因子：
6.2
通讯作者：
Chuang, Yung-Yu
中科院分区：
文献类型：
--
作者：
Li, Tzu-Mao;Wu, Yu-Ting;Chuang, Yung-Yu
我们将Stein的无偏风险估计器(Sure)应用于自适应采样和重建，以减少蒙特卡罗渲染中的噪声。Sure是统计学中均方误差(MSE)的一般无偏估计。可以肯定的是，我们能够估计任意重建核的误差，使我们能够使用更有效的核，而不是局限于以前工作中使用的对称核。它还允许我们将更多的样本分配到估计MSE较高的地区。因此，可以在优化框架内处理自适应采样和重建。我们还提出了一种高效且内存友好的方法来减少存在景深或运动模糊的噪声几何特征的影响。实验表明，与以往的方法相比，该方法生成的图像噪声更小，细节更清晰。
We apply Stein's Unbiased Risk Estimator (SURE) to adaptive sampling and reconstruction to reduce noise in Monte Carlo rendering. SURE is a general unbiased estimator for mean squared error (MSE) in statistics. With SURE, we are able to estimate error for an arbitrary reconstruction kernel, enabling us to use more effective kernels rather than being restricted to the symmetric ones used in previous work. It also allows us to allocate more samples to areas with higher estimated MSE. Adaptive sampling and reconstruction can therefore be processed within an optimization framework. We also propose an efficient and memory-friendly approach to reduce the impact of noisy geometry features where there is depth of field or motion blur. Experiments show that our method produces images with less noise and crisper details than previous methods.