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Robust Denoising using Feature and Color Information
Author(s) -
Rousselle Fabrice,
Manzi Marco,
Zwicker Matthias
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12219
Subject(s) - artificial intelligence , computer science , feature (linguistics) , computer vision , weighting , noise reduction , pattern recognition (psychology) , noise (video) , pixel , image (mathematics) , medicine , philosophy , linguistics , radiology
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‐theart. We also demonstrate adaptive sampling and space‐time filtering for animations.