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In this work, we report on our progress for investigating a new experimental approach for thermographic detection of internal defects by performing 2D photothermal super resolution reconstruction. We use modern high-power laser projector technology to repeatedly excite the sample surface photothermally with varying spatially structured 2D pixel patterns. In the subsequent (blind) numerical reconstruction, multiple measurements are combined by exploiting the joint-sparse nature of the defects within the specimen using nonlinear convex optimization methods. As a result, a 2D-sparse defect/inhomogeneity map can be obtained. Using such spatially structured heating combined with compressed sensing and computational imaging methods allows to significantly reduce the experimental complexity and to study larger test surfaces as compared to the one-dimensional approach reported earlier.
In a joint project of partners from industry and research, the automated recycling of construction and demolition waste (CDW) is investigated and tested by combing laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.). The project focuses primarily on the Berlin site to analyze the entire value chain, minimize economic/technological barriers and obstacles at the cluster level, and sustainably increase recovery and recycling rates. First measurements with LIBS and NIR spectroscopy show promising results in distinguishing various material types and indicate the potential for a successful combination. In addition, X-ray fluorescence (XRF) spectroscopy is being performed to obtain more information about the quantitative elemental composition of the different building materials. Future work will apply the developed sorting methodology in a fully automated measurement setup with CDW on a conveyor belt.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
Für die aktive Thermografie als zerstörungsfreie Prüfmethode galt lange Zeit die Faustformel, dass die Auflösung interner Defekte/Inhomogenitäten auf ein Verhältnis von Defekttiefe/Defektgröße ≤ 1 beschränkt ist. Die Ursache hierfür liegt in der diffusiven Natur der Wärmeleitung in Festkörpern. Sogenannte Super-Resolution-Ansätze erlauben seit Kurzem die Überwindung dieser physikalischen Grenze um ein Vielfaches. Damit ergibt sich die attraktive Möglichkeit die Thermografie von einem rein oberflächensensitiven Prüfverfahren hin zu einem Verfahren mit verbesserter Tiefenreichweite zu entwickeln. Wie weit diese Entwicklung getrieben werden kann, ist Gegenstand aktueller Forschung.
Wir konnten bereits zeigen, dass diese klassische Grenze für 1D- und 2D Defektgeometrien mit Hilfe des Abscannens des Prüfkörpers mittels einzelner Laserspots und der anschließenden Anwendung von photothermischer Super-Resolution-Rekonstruktion überwunden werden kann. Bei dieser Methode wird eine Kombination aus sequenzieller räumlich strukturierter Beleuchtung und numerischen Optimierungsmethoden eingesetzt. Dies geschieht allerdings auf Kosten der experimentellen Komplexität, die zu einer langen Messdauer, großen Datensätzen und langwieriger numerischer Auswertung führt.
In dieser Arbeit berichten wir über einen neuen experimentellen Ansatz, bei dem räumlich strukturierte 2D-Beleuchtungsmuster in Verbindung mit Compressed-Sensing und Computational-Imaging-Methoden verwendet werden, um die experimentelle Komplexität deutlich zu verringern und die Methode für die Untersuchung größerer Prüfflächen nutzbar zu machen.
Der experimentelle Ansatz basiert dabei auf der wiederholten (blinden) photothermischen Anregung mit räumlich strukturierten 2D-Mustern unter Verwendung moderner Projektortechnik und eines Hochleistungslasers. In der anschließenden numerischen Rekonstruktion werden mehrere Messungen unter Ausnutzung der Joint-Sparsity der Defekte innerhalb des Prüfkörpers mittels nichtlinearer konvexer Optimierungsmethoden kombiniert. Als Ergebnis kann eine 2D-sparse Defekt-/Inhomogenitätskarte erstellt werden.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.