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Media type:
E-Article
Title:
Progressive Denoising of Monte Carlo Rendered Images
Contributor:
Firmino, Arthur;
Frisvad, Jeppe Revall;
Jensen, Henrik Wann
imprint:
Wiley, 2022
Published in:Computer Graphics Forum
Language:
English
DOI:
10.1111/cgf.14454
ISSN:
0167-7055;
1467-8659
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>Image denoising based on deep learning has become a powerful tool to accelerate Monte Carlo rendering. Deep learning techniques can produce smooth images using a low sample count. Unfortunately, existing deep learning methods are biased and do not converge to the correct solution as the number of samples increase. In this paper, we propose a progressive denoising technique that aims to use denoising only when it is beneficial and to reduce its impact at high sample counts. We use Stein's unbiased risk estimate (SURE) to estimate the error in the denoised image, and we combine this with a neural network to infer a per‐pixel mixing parameter. We further augment this network with confidence intervals based on classical statistics to ensure consistency and convergence of the final denoised image. Our results demonstrate that our method is consistent and that it improves existing denoising techniques. Furthermore, it can be used in combination with existing high quality denoisers to ensure consistency. In addition to being asymptotically unbiased, progressive denoising is particularly good at preserving fine details that would otherwise be lost with existing denoisers.</jats:p>