Reconstructing temperature fields from limited infrared thermography data for in-plane thermal diffusivity inversion

Accurate reconstruction of complete temperature fields from sparsely sampled infrared thermography data is of great significance for reducing data acquisition burden, improving measurement efficiency, and enabling reliable thermal property inversion. This paper proposes a tensor completion algorithm based on multiple priors to reconstruct the complete temperature field from limited infrared thermography data. Reconstruction is achieved by enhancing three priors in the observed temperature data: tensor low-rankness, local smoothness, and non-local similarity. The effectiveness of the proposed algorithm is validated using experimentally acquired complete temperature field from a polyethylene terephthalate film. The results show that the proposed algorithm can accurately reconstruct the complete temperature field using only 10% of the original temperature data. The reconstructed temperature field enables extraction of the in-plane thermal diffusivity with an error of less than 5% compared with that obtained from the complete temperature field. Furthermore, the proposed method demonstrates competitive reconstruction accuracy and strong generalization capability compared with existing reconstruction algorithms.