Reference : Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of ...
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Aerospace & aeronautics engineering
Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection
Revel, Gian Marco mailto [Università Politecnica delle Marche > DIISM > > Professor]
Copertaro, Edoardo mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Chiariotti, Paolo mailto [Università Politecnica delle Marche > DIISM > > Researcher]
Pandarese, Giuseppe mailto [Università Politecnica delle Marche > DIISM > > Researcher]
D'Antuono, Antonio mailto [Università Politecnica delle Marche > DIISM > > PhD candidate]
Quantitative Infra Red Thermography Journal
Taylor & Francis
Yes (verified by ORBilu)
13th Quantitative Infrared Thermography Conference
July 4-8, 2016
[en] Infrared Testing ; Pulse Thermography ; Wavelet Denoising ; Optimisation Criteria
[en] 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.
H2020 ; 636063 - INSITER - Intuitive Self-Inspection Techniques using Augmented Reality for construction, refurbishment and maintenance of energy-efficient buildings made of prefabricated components

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