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

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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.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Institute of Computer Science (INF) > Computer Graphics Group (CGG)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Rousselle, Fabrice; Manzi, Marco and Zwicker, Matthias


000 Computer science, knowledge & systems
500 Science > 510 Mathematics






Matthias Zwicker

Date Deposited:

09 Apr 2014 11:14

Last Modified:

15 Aug 2017 09:32

Publisher DOI:





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