TY - INPR A1 - Ahmadi, Samim A1 - Lecompagnon, Julien A1 - Hirsch, Philipp Daniel A1 - Burgholzer, P. A1 - Jung, P. A1 - Caire, G. T1 - Laser excited super resolution thermal imaging for nondestructive inspection of internal defects N2 - A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques. KW - Super resolution KW - Photothermal KW - Imaging KW - Compressed sensing KW - Internal defects KW - Nondestructive testing PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-518866 DO - https://doi.org/10.48550/arXiv.2007.03341 SN - 2331-8422 SP - 1 EP - 9 PB - Cornell University CY - Ithaca, NY AN - OPUS4-51886 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ahmadi, Samim A1 - Thiel, Erik A1 - Karagianni, Christina A1 - Hirsch, Philipp Daniel A1 - Burgholzer, P. A1 - Mayr, G. A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Photothermal super resolution image reconstruction using structured 1D laser illumination N2 - The separation of two closely spaced defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern. This paper presents the results after applying super resolution algorithms, such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Different data processing techniques before applying the IJOSP algorithm as well as the influence of regularization parameters in the data processing techniques are discussed. Moreover, the degradation of super resolution reconstruction goodness by the choice of experimental parameters such as laser line width or number of measurements is shown. The application of the super resolution results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects. T2 - Quantitative Nondestructive Evaluation Conference 2019 CY - Portland, OR, USA DA - 14.07.2019 KW - Super resolution KW - Photothermal KW - Thermography KW - Laser PY - 2019 SP - Paper 8593, 1 PB - ASME AN - OPUS4-50924 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ahmadi, Samim A1 - Hirsch, Philipp Daniel A1 - Lecompagnon, Julien A1 - Hassenstein, Christian A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - New techniques in super resolution photothermal imaging for nondestructive testing N2 - In this work we focus on our most recent studies to super resolution (SR) laser thermography. The goal of SR nondestructive testing methods is to facilitate the separation of closely spaced defects. We explain how to combine laser scanning with SR techniques. It can be shown that stepwise as well as continuous scanning techniques are applicable. Finally, we discuss the effect of experimental parameters and im-age processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography. T2 - SMSI 2020 Conference CY - Online meeting DA - 22.06.2020 KW - Super resolution KW - Laser thermography KW - Nondestructive testing KW - Laser scanning KW - Photothermal imaging PY - 2020 DO - https://doi.org/10.5162/SMSI2020/C4.1 SP - 169 EP - 170 AN - OPUS4-50895 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Ahmadi, Samim A1 - Hauffen, Jan Christian A1 - Kästner, L. A1 - Jung, P. A1 - Caire, G. A1 - Ziegler, Mathias T1 - Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging 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. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data 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 than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging. KW - Iterative shrinkage thresholding algorithm KW - Neural network KW - Deep learning KW - Active thermography KW - Photothermal super resolution PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-525364 DO - https://doi.org/10.48550/arXiv.2012.03547 SN - 2331-8422 SP - 1 EP - 11 PB - Cornell University CY - Ithaca, NY AN - OPUS4-52536 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR 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 N2 - This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites. A grand challenge of active thermography is to overcome the spatial resolution limitation imposed by heat diffusion in order to accurately resolve each defect. The photothermal SR approach enables to extract high-frequency spatial components based on the deconvolution with the thermal point spread function. However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, hand-crafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, Photothermal-SR-Net is proposed in this paper, which performs deconvolution by deep unfolding considering the underlying physics. This enables to super resolve 2D thermal images for nondestructive testing with a substantially improved convergence rate. Since defects appear sparsely in materials, Photothermal-SR-Net applies trained blocksparsity thresholding to the acquired thermal images in each convolutional layer. The performance of the proposed approach is evaluated and discussed using various deep unfolding and thresholding approaches applied to 2D thermal images. Subsequently, studies are conducted on how to increase the reconstruction quality and the computational performance of Photothermal-SR-Net is evaluated. 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 - Photothermal super resolution KW - Nondestructive testing KW - Deep unfolding KW - Deep learning KW - Deep imaging KW - Physics-based deep learning KW - Laser thermography KW - Elastic net KW - Iterative shrinkage thresholding algorithm PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-525371 DO - https://doi.org/10.48550/arXiv.2104.10563 SN - 2331-8422 SP - 1 EP - 10 PB - Cornell University CY - Ithaca, NY AN - OPUS4-52537 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -