Convolution neural network based degenerated mode decomposition via near-field images from linear and circular polarizers

Abstract

Mode decomposition (MD) method for degenerated modes has been studied. Convolution neural network (CNN) has been applied for image training and predicting mode coefficients. Four-fold degenerated LP11 series has been the target to be decomposed. Total of seven different images, including full original near-field image, and images after linear polarizers of four directions (0°, 45°, 90°, and 135°), 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 mode coefficients, and the loss function has been selected to be root-mean-square (RMS) of labels. The RMS and mean-absolute-error (MAE) of the label, intensity, phase, and field correlation between 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 trained model was evaluated via 10,000 test samples with four sets of images - images after three linear polarizers (0°, 45°, 90°) 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 model considering only images after linear polarizers.