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Progressive path tracing with bilateral-filtering-based denoising

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Abstract

Path tracing can generate realistic images based on virtual 3D scene models, but the images are prone to be noisy. To solve this problem, we developed a novel denoising algorithm framework. Firstly, according to the relative mean square error of the noisy pixels, we introduced a progressive adaptive sampling strategy to optimize the distribution of samples. Next, to enhance the quality of the final reconstructed images, we designed an improved bilateral filtering algorithm with use of the gradient feature to obtain the noise-free images. Experimental results demonstrate that our framework outperforms the state-of-the-art path tracing denoising methods in terms of the visual quality, numerical error , and time cost.

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Acknowledgements

This work was supported partially by the National Natural Science Foundation of China under Grant U19A2063, partially by the Jilin Provincial Science & Technology Development Program of China under Grants 20170101005JC, 20180519012JH and 20190302113GX, partially by the 13th Five-year Science & Technology Research Program of Jilin Provincial Department of Education under Grant JJKH20200792KJ, and partially by Jilin provincial industrial innovation funds under Grant 2016C091.

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  1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China

    Qiwei Xing, Chunyi Chen & Zhihua Li

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  1. Qiwei Xing
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  2. Chunyi Chen
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  3. Zhihua Li
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Correspondence to Chunyi Chen.

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Xing, Q., Chen, C. & Li, Z. Progressive path tracing with bilateral-filtering-based denoising. Multimed Tools Appl 80, 1529–1544 (2021). https://doi.org/10.1007/s11042-020-09650-7

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