SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGHTEMPERATURE SCENES BASED ON NEURAL NETWORK

Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting thermal image temperature using color temperature measurements and an interpretable artificial neural network. This approach predicts temperatures in super-resolution thermal images ranging from 400 to 1200 degrees C. Through comparative validation, it is found that the three-neuron neural network approach demonstrates superior prediction accuracy compared to other machine learning methods. The seamlessly combined proposed super-resolution architecture with the temperature measurement method has a predicted RMSE of 20 degrees C for the whole temperature range with over 85% of samples falling within errors of 30 degrees C.