Temperature imaging network based on swin transformer for TDLAS tomography

Abstract

Most of existing data-driven temperature imaging schemes for Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography are based on Convolutional Neural Network (CNN). However, some studies on CNN show that its actual perceptual field is much smaller than the theoretical one, which makes it not conducive for CNN to capture features from contextual information at long distance. In this work, a temperature imaging network based on Swin Transformer is established. To introduce cross-window connections while maintaining the efficient computation of local non-overlapped windows, Multi-headed Self-Attention (MSA) is computed alternatively in regularly partitioned windows and shifted windows. Simulation results show that the proposed network can reconstruct temperature images of higher quality than schemes based on CNN and Extreme Learning Machine (ELM) respectively.