Robust Denoising using Feature and Color Information

Fabrice Rousselle1 Marco Manzi1 Matthias Zwicker1
1Computer Graphics Group, University of Bern

In Computer Graphics Forum (Proc. Pacific Graphics), 32(7), October 2013

We propose a method to denoise Monte Carlo renderings using noisy color (left) and feature buffers (middle: texture, caustics, visibility, normals) as an input. We construct a denoising filter by combining color and feature information using a SURE error estimate. Our results (right) improve visually and quantitatively over the previous state-of-the-art.


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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