loading
Papers

Research.Publish.Connect.

Paper

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

ISBN: 978-989-758-287-5

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 quality of the visual image and the numerical error incurred. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.81.29.226

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 1 GRAPP: GRAPP, ISBN 978-989-758-287-5, 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 - Volume 1 GRAPP: GRAPP,},
year={2018},
pages={288-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006650602880294},
isbn={978-989-758-287-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1 GRAPP: GRAPP,
TI - Denoising Monte Carlo Renderings based on a Robust High-order Function
SN - 978-989-758-287-5
AU - Liu, Y.
AU - Zheng, C.
AU - Yuan, H.
PY - 2018
SP - 288
EP - 294
DO - 10.5220/0006650602880294

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.