# Quaternion Processing Techniques for Color Synthesized NDT Thermography
###### Abstract
This situation makes the experience and skill of the inspector decisive in the analysis stage and reveals a high dependency on the human factor. This study was conducted to deal with these limitations, with the objective of advancing in the definition of the correct protocol for defects characterization, improving at the same time the detection capacity of IRT NDT. For this purpose an innovative processing methodology is proposed, which makes use of colorization techniques to enhance the information gathered in the original thermographic sequences. Thermographic images are intrinsically monochromatic since IR sensors provide intensity values, commonly digitized to 14-bit data by an A/D converter after the capture stage, which correspond to the radiation levels received from the measuring point within their sensitive spectral range. Depending on the type of sensor, different spatial resolution may be available in the measurements, but each pixel will always provide a scalar value over time. Therefore, thermographic images are naturally represented through intensity images by means of grayscale images and false color images, adapting the measured radiometric values to the desired dynamic range for correct visualization. Several studies have investigated color thermographic images to process IRT NDT data and improve the defect detection efficiency of the technique. There are two general research lines related to color thermographic images. In one line, authors apply colormaps to the raw thermographic signals to produce false color thermographic images and then apply conventional color image processing techniques to extract information of interest. The authors in [15] take the values contained in the red channel of the false color thermographic images produced by the camera software and use it as indicator of hot areas for defects location. In this case, any other colormap with a different red color values distribution would provide different results. The authors in [16; 17] apply a color space transformation to the original false color thermographic images to process the thermographic values in the HSI and HSV color spaces. The hue is considered the key information for segmentation and defect detection, but this magnitude is dependent on the colormap so different hue distributions provide different results. This analysis approach degrades the original information, drastically reducing the quantity and quality of the information available in the analysis since the raw signal is arbitrary split into three color ranges that depend on the specific colormap being used. The results obtained in these studies are outperformed by processing the raw intensity IR images with grayscale processing methods such as image filtering and contrast adjustment. The results produced are ad-hoc and dependent on the choice of the colormap to transform the original intensity signals to produce the three channel color images. However, very few of these studies indicate the colormap used in the analysis. In the other line of research related to color thermographic images, authors take advantage of the higher capacity to store information provided by the 3 channels in true color images to include independent information and make the results more informative. The true color channels are used as holders where information obtained from different processing techniques applied to the original IR data are deposited. The initial precursor of this research line was [PERSON] et al. in [18,19] where the channels of the RGB color space were included with the coefficients obtained from the application of the well-known Thermographic Signal Reconstruction (TSR) algorithm to produce a better characterization of defects by the resulting colors. Afterwards, [PERSON] et al. in [20] validated the RGB projection methodology for results obtained from different conventional processing algorithms used in IRT NDT such as spectral analysis, principal components analysis or higher order statistics, among others. More recently, [PERSON] et al.