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Noise Reduction on G-Buffers for Monte Carlo Filtering: Noise Reduction on G-Buffers for Monte Carlo Filtering

Moon, Bochang; Iglesias-Guitian, Jose A.; McDonagh, Steven; Mitchell, Kenny

Authors

Bochang Moon

Jose A. Iglesias-Guitian

Steven McDonagh



Abstract

We propose a novel pre-filtering method that reduces the noise introduced by depth-of-field and motion blur effects in geometric
buffers (G-buffers) such as texture, normal and depth images. Our pre-filtering uses world positions and their variances to
effectively remove high-frequency noise while carefully preserving high-frequency edges in the G-buffers. We design a new
anisotropic filter based on a per-pixel covariance matrix of world position samples. A general error estimator, Stein’s unbiased
risk estimator, is then applied to estimate the optimal trade-off between the bias and variance of pre-filtered results. We have
demonstrated that our pre-filtering improves the results of existing filtering methods numerically and visually for challenging
scenes where depth-of-field and motion blurring introduce a significant amount of noise in the G-buffers.

Citation

Moon, B., Iglesias-Guitian, J. A., McDonagh, S., & Mitchell, K. (2017). Noise Reduction on G-Buffers for Monte Carlo Filtering: Noise Reduction on G-Buffers for Monte Carlo Filtering. Computer Graphics Forum, 36(8), 600-612. https://doi.org/10.1111/cgf.13155

Journal Article Type Article
Acceptance Date Dec 12, 2016
Online Publication Date May 23, 2017
Publication Date May 23, 2017
Deposit Date Jun 23, 2017
Journal Computer Graphics Forum
Print ISSN 0167-7055
Electronic ISSN 1467-8659
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 36
Issue 8
Pages 600-612
DOI https://doi.org/10.1111/cgf.13155
Keywords Image filtering, denoising, Monte Carlo ray tracing,
Public URL http://researchrepository.napier.ac.uk/Output/951658