Method for three-view extraction and particle 3D distribution reconstruction of trichromatic mask PIV

A method for three-view extraction and particle three-dimensional (3D) distribution reconstruction of trichromatic mask particle image velocimetry (PIV), comprising:

• shooting a color particle image;
• separating color channels of the color particle image by using a demosaicing algorithm based on a U-Net++ neural network, to obtain a three-view particle grayscale image, wherein the U-Net++ neural network comprises a first output layer, a second output layer, and a third output layer with extraction depth increasing sequentially;
• performing preliminary 3D reconstruction on the three-view particle grayscale image by using a multiplicative line-of-sight (MLOS) algorithm, to obtain an initial voxel field;
• and
• performing 3D reconstruction refining on the initial voxel field by using a trained convolutional neural network model, to obtain a fine voxel field, wherein
• a process of training the convolutional neural network model comprises:
• setting optical parameters and calculating a projection transformation matrix according to the optical parameters;
• setting a plurality of particle concentrations, and randomly generating an equal particle spatial intensity voxel field according to the particle concentrations;
• converting the equal particle spatial intensity voxel field into a to-be-trained two-dimensional (2D) particle grayscale image by using the projection transformation matrix;
• performing preliminary 3D reconstruction on the to-be-trained 2D particle grayscale image by using the MLOS algorithm, to obtain a to-be-trained voxel field;
• randomly dividing the to-be-trained voxel field according to a preset proportion into a training set, a verification set, and a test set;
• constructing a network model, wherein the network model comprises a plurality of hidden network layers comprising a 3D convolution layer, a batch normalization layer, and an activation function layer;
• constructing a first loss function;
• training, according to the training set and the verification set, the network model using an adaptive moment estimation (Adam) optimizer and an exponential moving average (EMA) method, to obtain a trained to-be-tested model;
• testing the to-be-tested model by using the test set, to obtain a test result;
• and
• outputting the to-be-tested model if the test result meets a preset standard, to obtain the convolutional neural network model.