Removing Monte Carlo Noise with Compressed Sensing and Feature Information

Changwen Zheng, Yu Liu

Abstract

Monte Carlo renderings suffer noise artifacts at low sampling rates. In this paper, a novel rendering algorithm that combines compressed sensing (CS) and feature buffers is proposed to remove the noise. First, in the sampling stage, the image is divided into patches that each one corresponds to a fixed resolution. Second, each pixel value in the patch is reconstructed by calculating the related coefficients in a transform domain, which is achieved by a CS-based algorithm. Then in the reconstruction stage, each pixel is filtered over a set of filters that use a combination of colors and features. The difference between the reconstructed value and the filtered value is used as the estimated reconstruction error. Finally, a weighted average of two filters that return the smallest error is computed to minimize output error. The experimental results show that the new algorithm outperforms previous methods both in visual image quality and numerical error.

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Paper Citation


in Harvard Style

Zheng C. and Liu Y. (2018). Removing Monte Carlo Noise with Compressed Sensing and Feature Information.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 145-153. DOI: 10.5220/0006671601450153


in Bibtex Style

@conference{grapp18,
author={Changwen Zheng and Yu Liu},
title={Removing Monte Carlo Noise with Compressed Sensing and Feature Information},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,},
year={2018},
pages={145-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006671601450153},
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 - Removing Monte Carlo Noise with Compressed Sensing and Feature Information
SN - 978-989-758-287-5
AU - Zheng C.
AU - Liu Y.
PY - 2018
SP - 145
EP - 153
DO - 10.5220/0006671601450153