Efficient learning representation of noise-reduced foam effects with convolutional denoising networks

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks

This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem that occurs in this process is prevented by using...

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Bibliographic Details
Main Authors:	Jong-Hyun Kim, YoungBin Kim
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2022-01-01
Series:	PLoS ONE
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9551625/?tool=EBI

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