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Journal of System Simulation

Abstract

Abstract: Aiming at the rendering noise in Monte Carlo synthesized images induced by the low light-path sampling rate, a denoising algorithm based on the multi-feature non-local-mean filtering is proposed. The gradient image of the scene's albedo information is calculatedwith the canny operator, and a guided filter together with the said gradient image is employed to prefilter the normal vector image. The structural similarity of the sub-blocks in the prefiltered normal vector image is calculated and the improved weights of the non-local mean filter are computed according to the logarithmic value of the reciprocal of the structural similarity. The improved non-local mean filter is used to implement the reconstruction of the noisy image. The experimental results show that the proposed algorithm can effectively reduce the level of Monte Carlo rendering noise in typical scenes, and can improve the metrics of mean square error and peak signal-to-noise ratio.

First Page

1259

Revised Date

2021-01-28

Last Page

1266

CLC

TP391.9

Recommended Citation

Kai Yang, Chunyi Chen, Xiaojuan Hu, Haiyang Yu. Denoising Algorithm Based on Multi-feature Non-local Mean Filtering for Monte Carlo Rendered Images[J]. Journal of System Simulation, 2022, 34(6): 1259-1266.

Corresponding Author

Chunyi Chen,chenchunyi@hotmail.com

DOI

10.16182/j.issn1004731x.joss.20-1037

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