Particle image enhancement method based on dual-branch residual network
A dual-branch residual convolutional neural network was proposed for image enhancement in PIV velocity tech-nique 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.Sec-ondly,a challenging image enhancement dataset was autono-mously generated to train model parameters,including Gaussi-an 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,high-er 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.
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