Quantitative Pulsed Phase Thermography (PPT) has been only used to estimate defect parameters such as depth and thermal resistance. Here, we propose a thermal quadrupole based method that extends quantitative pulsed phase thermography. This approach estimates thermal diffusivity by solving a inversion problem based on non-linear squares estimation. This approach is tested with pulsed thermography data acquired from a composite sample. We compare our results with another technique established in time domain. The proposed quantitative analysis with PPT provides estimates of thermal diffusivity close to those obtained with the time domain approach. This estimation requires only the a priori knowledge of sample thickness.
Infrared (IR) images are representations of the world and have natural features like images in the visible spectrum.
As such, natural features from infrared images support image quality assessment (IQA).1
In this work, we
compare the quality of a set of indoor and outdoor IR images reconstructed from measurement functions formed
by linear combination of their pixels. The reconstruction methods are: linear discrete cosine transform (DCT)
acquisition, DCT augmented with total variation minimization, and compressive sensing scheme. Peak Signal to
Noise Ratio (PSNR), three full-reference (FR), and four no-reference (NR) IQA measures compute the qualities
of each reconstruction: multi-scale structural similarity (MSSIM), visual information fidelity (VIF), information
fidelity criterion (IFC), sharpness identification based on local phase coherence (LPC-SI), blind/referenceless
image spatial quality evaluator (BRISQUE), naturalness image quality evaluator (NIQE) and gradient singular
value decomposition (GSVD), respectively. Each measure is compared to human scores that were obtained by
differential mean opinion score (DMOS) test. We observe that GSVD has the highest correlation coefficients of
all NR measures, but all FR have better performance. We use MSSIM to compare the reconstruction methods
and we find that CS scheme produces a good-quality IR image, using only 30000 random sub-samples and 1000
DCT coefficients (2%). In contrast, linear DCT provides higher correlation coefficients than CS scheme by using
all the pixels of the image and 31000 DCT (47%) coefficients.