Robust Denoising using Feature and Color Information

Rousselle, Fabrice; Manzi, Marco; Zwicker, Matthias (2013). Robust Denoising using Feature and Color Information. Computer graphics forum, 32(7), pp. 121-130. Wiley 10.1111/cgf.12219

Text cgf12219.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (16MB)

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On
one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising
filters because features are highly correlated with rendered images. Filters based solely on features, however, are
prone to blurring image details that are not well represented by the features. On the other hand, color buffers
represent all details, but they may be less effective to determine filters because they are contaminated by the noise
that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an
NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using
a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared
to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

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