[1]LU Juan,CHEN Chunyi.A denoised method by fusing recursive auto-encoder decorder for Monte Carlo rendering image reconstruction[J].CAAI Transactions on Intelligent Systems,2023,18(3):459-467.[doi:10.11992/tis.202112011]
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A denoised method by fusing recursive auto-encoder decorder for Monte Carlo rendering image reconstruction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 459-467 Column: 学术论文—机器学习 Public date: 2023-07-05
Title:
A denoised method by fusing recursive auto-encoder decorder for Monte Carlo rendering image reconstruction
Author(s):
LU Juan;  CHEN Chunyi
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
Keywords:
recursive residual network; multi-scale convolution; generative adversarial networks; Monte Carlo rendering screen; image processing; image denoising; deep learning; auto-encoder network
CLC:
TP183
DOI:
10.11992/tis.202112011
Abstract:
Aiming at the problem of artifacts and blurring in the noise reduction results of generative adversarial model, a noise reduction method for Monte Carlo rendering image based on generative adversarial recurrent auto-encoder is proposed in this paper. On the model, the multi-scale convolutional coding structure is designed, and the multi-scale residual auto-encoder model is used as the generator. The feature extraction at different levels is realized by combination connection, fusing the feature information of different receptive fields. The designed recursive residual network model is used as the discriminator to judge the authenticity of the Monte Carlo rendering picture and improve the network performance in the confrontation. In an end-to-end way, the auxiliary information features, the noise-containing Monte Carlo pictures and the pictures with the sampling rate of 8192 are input into the recursive auto-encoder decoder for noise reduction. Experiments show that the average peak signal-to-noise ratio of this method is 32.44 dB in the test scenario, which is 4.80% and 3.13% higher than the generative adversarial network method and the residual network method, respectively. The average structural similarity is 0.92, which is 2.54 % and 1.01 % higher than the existing two algorithms, respectively.
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