Optimized Gaussian model for non-uniform heating compensation in pulsed thermography.

This paper presents a new, to the best of our knowledge, methodology for the thermal compensation of background heating in thermograms of composites. The technique analyzes the spatial data of the thermal images obtained from a pulsed thermography inspection and automatically calculates the optimal parameters of a predefined objective function. These parameters are obtained by curve fitting using the least squares method and model the temperature distribution of the image background using the proposed objective function. To verify the methodology, we use real and synthetic images of a sample of carbon-fiber-reinforced plastic (CFRP) with defects, with diameter/depth ratios that range between 15.0 and 75.0 and between 1.7 and 90.0, respectively. The performance of the method is tested using a local and a global definition of the signal-to-noise ratio (SNR) and is statistically validated by analysis of variance. The average performance values obtained were 55.0dB and 7.0dB on synthetic images and real images, respectively. The proposed method provides superior and statistically significant differences compared to techniques reported in the literature for contrast enhancement [e.g., differential absolute contrast (DAC) and background thermal compensation by filtering (BTCF)]. Unlike contrast normalization (CN), the proposed technique stands out since it does not need to predefine variables, select reference regions, have prior knowledge of the partial (or complete) state of the material, or analyze totally (or partially) the temporal evolution of the temperature or any characteristic derived from it.