---
OA_place: publisher
OA_type: hybrid
_id: '19028'
abstract:
- lang: eng
text: The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably
produces noise in rendered images for a practical number of samples per pixel.
The problem of denoising these images has been widely studied, with most recent
methods relying on data-driven, pretrained neural networks. In contrast, in this
paper we propose a statistical approach to the denoising problem, treating each
pixel as a random variable and reasoning about its distribution. Considering a
pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based
on online estimators—to decide which of the nearby pixels to exclude from the
denoising filter. We show that for symmetric pixel weights and normally distributed
samples, the classical Welch t-test is optimal in terms of mean squared error.
We then show how to extend this result to handle non-normal distributions, using
more recent confidence-interval formulations in combination with the Box-Cox transformation.
Our results show that our statistical denoising approach matches the performance
of state-of-the-art neural image denoising without having to resort to any computation-intensive
pretraining. Furthermore, our approach easily generalizes to other quantities
besides pixel intensity, which we demonstrate by showing additional applications
to Russian roulette path termination and multiple importance sampling.
acknowledgement: 'We would like to thank Lukas Lipp for fruitful discussions, Károly
Zsolnai-Fehér and Jaroslav Křivánek for valuable contributions to early versions
of this work, and Bernhard Kerbl for help with our CUDA implementation. Moreover,
we would like to thank the creators of the scenes we have used: Wig42 for “Wooden
Staircase” (Fig. 1), “Grey and White Room” (Fig. S6), and “Modern Living Room” (Fig.
S8); nacimus for “Bathroom” (Fig. 3, S5); NovaZeeke for “Japanese Classroom” (Fig.
4, 6); Beeple for “Zero-Day” (Fig. 8); Jay-Artist for “White Room” (Fig. S7); Mareck
for “Contemporary Bathroom” (Fig. 2); Christian Freude for “Glass Caustics” (Fig.
S10); and Benedikt Bitterli for “Veach Ajar” (Fig. 7, S2), “Veach MIS” (Fig. S4),
and “Fur Ball” (Fig. S11). This work has received funding from the Vienna Science
and Technology Fund (WWTF) project ICT22-028 (“Toward Optimal Path Guiding for Photorealistic
Rendering”) and the Austrian Science Fund (FWF) project F 77 (SFB “Advanced Computational
Design”).'
article_number: '68'
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Hiroyuki
full_name: Sakai, Hiroyuki
last_name: Sakai
- first_name: Christian
full_name: Freude, Christian
last_name: Freude
- first_name: Thomas
full_name: Auzinger, Thomas
id: 4718F954-F248-11E8-B48F-1D18A9856A87
last_name: Auzinger
orcid: 0000-0002-1546-3265
- first_name: David
full_name: Hahn, David
id: 357A6A66-F248-11E8-B48F-1D18A9856A87
last_name: Hahn
- first_name: Michael
full_name: Wimmer, Michael
last_name: Wimmer
citation:
ama: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. A statistical approach to
Monte Carlo denoising. In: Proceedings - SIGGRAPH Asia 2024 Conference Papers.
Association for Computing Machinery; 2024. doi:10.1145/3680528.3687591'
apa: 'Sakai, H., Freude, C., Auzinger, T., Hahn, D., & Wimmer, M. (2024). A
statistical approach to Monte Carlo denoising. In Proceedings - SIGGRAPH Asia
2024 Conference Papers. Tokyo, Japan: Association for Computing Machinery.
https://doi.org/10.1145/3680528.3687591'
chicago: Sakai, Hiroyuki, Christian Freude, Thomas Auzinger, David Hahn, and Michael
Wimmer. “A Statistical Approach to Monte Carlo Denoising.” In Proceedings -
SIGGRAPH Asia 2024 Conference Papers. Association for Computing Machinery,
2024. https://doi.org/10.1145/3680528.3687591.
ieee: H. Sakai, C. Freude, T. Auzinger, D. Hahn, and M. Wimmer, “A statistical approach
to Monte Carlo denoising,” in Proceedings - SIGGRAPH Asia 2024 Conference Papers,
Tokyo, Japan, 2024.
ista: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. 2024. A statistical approach
to Monte Carlo denoising. Proceedings - SIGGRAPH Asia 2024 Conference Papers.
SA: SIGGRAPH Asia, 68.'
mla: Sakai, Hiroyuki, et al. “A Statistical Approach to Monte Carlo Denoising.”
Proceedings - SIGGRAPH Asia 2024 Conference Papers, 68, Association for
Computing Machinery, 2024, doi:10.1145/3680528.3687591.
short: H. Sakai, C. Freude, T. Auzinger, D. Hahn, M. Wimmer, in:, Proceedings -
SIGGRAPH Asia 2024 Conference Papers, Association for Computing Machinery, 2024.
conference:
end_date: 2024-12-06
location: Tokyo, Japan
name: 'SA: SIGGRAPH Asia'
start_date: 2024-12-03
date_created: 2025-02-16T23:02:34Z
date_published: 2024-12-03T00:00:00Z
date_updated: 2025-12-02T13:58:56Z
day: '03'
ddc:
- '000'
doi: 10.1145/3680528.3687591
external_id:
isi:
- '001441591200068'
file:
- access_level: open_access
checksum: 89f63b9237224362ec33430af9152700
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creator: dernst
date_created: 2025-04-15T12:53:24Z
date_updated: 2025-04-15T12:53:24Z
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file_name: 2024_SIGGRAPH_Sakai.pdf
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file_date_updated: 2025-04-15T12:53:24Z
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language:
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month: '12'
oa: 1
oa_version: Published Version
publication: Proceedings - SIGGRAPH Asia 2024 Conference Papers
publication_identifier:
isbn:
- '9798400711312'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: A statistical approach to Monte Carlo denoising
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...