Denoising Algorithm Based on Multi-feature Non-local Mean Filtering for Monte Carlo Rendered Images

doi:10.16182/j.issn1004731x.joss.20-1037

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.

Key words: Monte Carlo, rendering, guide filter, non-local mean filter, structural similarity

CLC Number:

TP391.9

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.

Figures/Tables 10

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References 21

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搜索框与相似块	PSNR	MSE
5×5、3×3	33.584 6	0.001 20
7×7、5×5	35.226 7	0.000 98
11×11、5×5	33.660 3	0.001 20
21×21、7×7	34.055 8	0.001 30

算法	PSNR	MSE
MC	28.358 7	0.004 30
NLM	30.066 2	0.003 20
文献[11]	34.590 7	0.000 34
本文	36.009 3	0.000 74

算法	PSNR	MSE
MC	27.482 9	0.005 50
NLM	32.742 1	0.003 50
文献[11]	34.413 0	0.000 86
本文	34.495 2	0.001 10

算法	PSNR	MSE
MC	24.843 7	0.009 90
NLM	30.066 2	0.002 90
文献[11]	32.894 5	0.000 51
本文	34.895 4	0.000 98

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