Denoising Monte Carlo Renderings based on a Robust High-order Function

Yu Liu, Changwen Zheng, Hongliang Yuan

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.

Download


Paper Citation


in Harvard Style

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, ISBN 978-989-758-287-5, pages 288-294. DOI: 10.5220/0006650602880294


in Bibtex Style

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


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: 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