Acoustic tomography temperature reconstruction based on improved sparse reconstruction model and multi-scale feature fusion network.

Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting algorithm, and the alternating direction method of multipliers algorithm (ADMM) is used to solve the model to obtain the initial temperature distribution. Then the feature extraction network is constructed to extract multi-scale features of acoustic time of flight (TOF) as prior information. Finally, the feature fusion reconstruction network is constructed to fuse and reconstruct the initial temperature distribution and multi-scale features to obtain a high-precision temperature distribution. Simulation and experimental tests were conducted respectively, and compared with other algorithms. The results show that the average relative error and root mean square error of the simulated temperature distribution reconstruction are 0.073% and 0.1% respectively, the average reconstruction error of the temperature points set in the experimental test is 0.38%, and the reconstruction errors are lower than other algorithms. The proposed method effectively utilizes prior information to correct sparse reconstruction temperature distribution reconstruction results, significantly improving the quality of temperature distribution reconstruction. [Display omitted] • Acoustic tomography technology is used for temperature distribution reconstruction. • The improved sparse reconstruction model can improve the accuracy of initial temperature distribution reconstruction. • The feature extraction network can perform multi-scale feature extraction on the time-of-flight of acoustic waves. • The feature fusion reconstruction network can improve the utilization of prior information. [ABSTRACT FROM AUTHOR]