Stationary Wavelet Transform denoising in Pulsed Thermography: influence of camera resolution on defect detection

Denoising filters are widely used in image enhancement. However, they might induce severe blurring effects the lower is the resolution of the original image. When applied to a thermal image in Non-Destructive Testing (NDT), blurring could entail wrong estimation of defect boundaries and an overall reduction in defect detection performances. This contribution discusses the application of a wavelet-based denoising technique to a thermographic sequence obtained from a Pulsed Thermography testing, when using a high- resolution 1024x768 FPA infrared camera. Influence of denoising approach on data post- processed by Principal Component Analysis is discussed. Results indicate marked enhancement in defect detection, especially when compared to those obtained with a standard-resolution 320x240 FPA infrared camera

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion

Characterization of porosity and defects on composite materials using X-ray computed tomography and image processing

This paper deals with the development of a measurement procedure to characterize anomalies, i.e. voids and defects, in four composite material (CM) samples. For this aim, four CM samples, each of them characterized by specific manufacturing techniques, have been analyzed. The first one (CM1) has Teflon defects, the second one (CM2) has undergone a low-degree manufacturing process and thus judged too porous at quality control, the third one (CM3) has passed the interlaminar shear strength (ILSS) test and so is expected to have a low-level of anomalies, unlike the fourth one (CM4), which has failed at ILSS test. An industrial X-ray computed tomography (CT) has been used to scan the CM samples and a specific image processing technique has been developed to measure the number and dimension of anomalies within them. The calculated amount of anomalies seems to be within the acceptable range identified in literature, always below 5%, showing the goodness of manufacturing process, and furthermore a threshold level of 0.09 mm has been statistically calculated to discriminate between voids and the other kinds of defects