Particle image enhancement method based on dual-branch residual network

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  • (1. College of Artificial Intelligence, Dalian Maritime University, Dalian  116026, China; 2. College of Software, Liaoning Technical University,Huludao 125000, China;3. College of Shipbuilding Engineering, Harbin Engineering University,  Harbin  150001, China )

Online published: 2024-05-24

Abstract

A dual-branch residual convolutional neural network was proposed for image enhancement in PIV velocity technique to obtain high-quality particle images. Firstly, a dual-branch convolutional neural network composed of residual blocks was designed to extract features from the input particle image pairs, while a coding-decoder was used to effectively fuse the feature information of the particle image pairs. Secondly, a challenging image enhancement dataset was autonomously generated to train model parameters, including Gaussian noises of different concentrations, light intensity noise and various real interference backgrounds, thereby fully simulating real fluid scenes. Results show that the proposed method can effectively deal with noise interference in both synthesized and real images, achieving image enhancement. Meanwhile, higher precision velocity fields can be obtained by using velocity field estimation algorithm to process the particle image pairs enhanced by the proposed method in this paper.

Cite this article

ZHANG Zhihao, YU Changdong, LIU Baisheng, FAN Yiwei . Particle image enhancement method based on dual-branch residual network[J]. Journal of Dalian Maritime University, 2024 , 50(4) : 100 -109 . DOI: 10.16411/j.cnki.issn1006-7736.2024.04.011

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