TY - JOUR A1 - Ahmadi, Samim A1 - Kästner, L. A1 - Hauffen, Jan Christian A1 - Jung, P. A1 - Ziegler, Mathias T1 - Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging JF - IEEE Transactions on Instrumentation and Measurement N2 - This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step. KW - Deep unfolding KW - Defect reconstruction KW - Elastic net KW - Inverse problems KW - Iterative shrinkage thresholding KW - Neural network KW - Nondestructive testing (NDT) KW - Photothermal imaging KW - Super resolution (SR) KW - Thermography PY - 2022 DO - https://doi.org/10.1109/tim.2022.3154803 SN - 1557-9662 VL - 71 SP - 1 EP - 9 PB - IEEE AN - OPUS4-54678 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hauffen, J. C. A1 - Kästner, L. A1 - Ahmadi, Samim A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging JF - Sensors N2 - 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. KW - Thermography KW - Laser KW - Machine learning KW - Optimization KW - Non-destructive testing KW - NDT KW - Neural network KW - Defect reconstruction KW - Block-sparsity KW - Active thermal imaging KW - Regularization KW - Laser thermography PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554598 DO - https://doi.org/10.3390/s22155533 SN - 1424-8220 VL - 22 IS - 15 SP - 1 EP - 15 PB - MDPI CY - Basel AN - OPUS4-55459 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lecompagnon, Julien A1 - Ahmadi, Samim A1 - Hirsch, Philipp Daniel A1 - Rupprecht, C. A1 - Ziegler, Mathias T1 - Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating JF - Journal of Applied Physics N2 - 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. KW - Thermography KW - Super resolution KW - NDT KW - Inspection KW - Internal defects PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-548351 DO - https://doi.org/10.1063/5.0088102 SN - 1089-7550 VL - 131 IS - 18 SP - 1 EP - 12 PB - AIP Publishing AN - OPUS4-54835 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lecompagnon, Julien A1 - Hirsch, Philipp Daniel A1 - Rupprecht, C. A1 - Ziegler, Mathias ED - Mendioroz, A. ED - Avdelidis, N. P. T1 - Thermographic testing using 2D pseudo-random illumination and photothermal super resolution reconstruction T2 - Proceedings of SPIE N2 - Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed. T2 - Thermosense: Thermal Infrared Applications XLIV CY - Orlando, Florida, USA DA - 05.04.2022 KW - Thermography KW - Super resolution KW - NDT KW - Material testing KW - Internal defects KW - DMD KW - DLP PY - 2022 DO - https://doi.org/10.1117/12.2618562 SN - 0277-786X VL - 12109 SP - 1 EP - 10 PB - SPIE AN - OPUS4-54909 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -