论文标题
基于卷积神经网络的模式分解，用于通过极化器的多个图像进行退化模式
Convolution Neural Network based Mode Decomposition for Degenerated Modes via Multiple Images from Polarizers
论文作者
论文摘要
在本文中，已经研究了用于退化模式的模式分解方法。卷积神经网络（CNN）已应用于图像训练并预测模式系数。四倍退化的$ lp_ {11} $系列是要分解的目标。多个图像被认为是分解退化模式的输入。共有七个不同的图像，包括完整的原始近场图像，以及四个方向的线性极化器之后的图像（0 $^\ circ $，45 $^\ circ $，90 $^\ circ $和135 $^\ circ $），以及两个循环极化器后的图像（右手和左手）后进行了训练，验证，验证，验证，验证，验证，验证，测试，和测试。该模型的输出标签已选择为模式系数的真实和虚构组件，并且已选择损耗函数为标签的根平方（RMS）。已选择了实际值和预测值之间标签，强度，相位和场相关的RMS和均值误差（MAE）是评估CNN模型的指标。 CNN模型已通过100,000个三维图像进行培训，深度为三，四和七。通过10,000个测试样品评估了训练有素的模型的性能 - 三个线性极化器（0 $^\ circ $，45 $^\ circ $，90 $^\ circ $）和右手圆极偏光剂的图像显示0.0634标记RMS，0.0292 of Intermitsion RMS，0.1867的cromer and MAE，和0.1867 rad of Serper of Spears 7和0.0634，并显示了0.0634的图像。与仅考虑线性极化器后仅考虑图像的模型相比，4个图像集的性能至少显示了50.68％的性能增强。
In this paper, a mode decomposition (MD) method for degenerated modes has been studied. Convolution neural network (CNN) has been applied for image training and predicting the mode coefficients. Four-fold degenerated $LP_{11}$ series has been the target to be decomposed. Multiple images are regarded as an input to decompose the degenerate modes. Total of seven different images, including the full original near-field image, and images after linear polarizers of four directions (0$^\circ$, 45$^\circ$, 90$^\circ$, and 135$^\circ$), and images after two circular polarizers (right-handed and left-handed) has been considered for training, validation, and test. The output label of the model has been chosen as the real and imaginary components of the mode coefficient, and the loss function has been selected to be the root-mean-square (RMS) of the labels. The RMS and mean-absolute-error (MAE) of the label, intensity, phase, and field correlation between the actual and predicted values have been selected to be the metrics to evaluate the CNN model. The CNN model has been trained with 100,000 three-dimensional images with depths of three, four, and seven. The performance of the trained model was evaluated via 10,000 test samples with four sets of images - images after three linear polarizers (0$^\circ$, 45$^\circ$, 90$^\circ$) and image after right-handed circular polarizer - showed 0.0634 of label RMS, 0.0292 of intensity RMS, 0.1867 rad of phase MAE, and 0.9978 of average field correlation. The performance of 4 image sets showed at least 50.68\% of performance enhancement compared to models considering only images after linear polarizers.
