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Authors: Yu Liu 1 ; Changwen Zheng 2 and Hongliang Yuan 1

Affiliations: 1 Chinese Academy of Sciences and University of Chinese Academy of Sciences, China ; 2 Chinese Academy of Sciences, China

Keyword(s): Adaptive Rendering, Image Space Reconstruction, Guided Image Filter, Mean Squared Error.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image-Based Modeling ; Image-Based Rendering ; Pattern Recognition ; Physics-Based Modeling ; Rendering ; Rendering Algorithms ; Software Engineering

Abstract: Image space rendering methods are efficient at removing Monte Carlo noise. However, a major challenge is optimizing the bandwidth to denoise images while preserving their fine details. In this paper, a high-order function is proposed to leverage the correlation between features and pixel colors. We consider feature buffers to fit data while computing regression weights using pixel colors. A collaborative prefiltering framework is first proposed to denoise features. The input pixel colors are then denoised using a guided image filter that maintains fine details in the output by constructing a guidance image using features. The optimal bandwidth is selected through an iterative error estimation process performed at multiple pixels to smooth the details. Finally, we adaptively select center pixels to build our regression models and vary the window size to reduce computational overhead. Experimental results showed that the new approach outperforms competing methods in terms of the qualit y of the visual image and the numerical error incurred. (More)

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Paper citation in several formats:
Liu, Y.; Zheng, C. and Yuan, H. (2018). Denoising Monte Carlo Renderings based on a Robust High-order Function. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - GRAPP; ISBN 978-989-758-287-5; ISSN 2184-4321, SciTePress, pages 288-294. DOI: 10.5220/0006650602880294

@conference{grapp18,
author={Yu Liu. and Changwen Zheng. and Hongliang Yuan.},
title={Denoising Monte Carlo Renderings based on a Robust High-order Function},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - GRAPP},
year={2018},
pages={288-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006650602880294},
isbn={978-989-758-287-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - GRAPP
TI - Denoising Monte Carlo Renderings based on a Robust High-order Function
SN - 978-989-758-287-5
IS - 2184-4321
AU - Liu, Y.
AU - Zheng, C.
AU - Yuan, H.
PY - 2018
SP - 288
EP - 294
DO - 10.5220/0006650602880294
PB - SciTePress