TY - GEN 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 T2 - arxiv.org 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 UR - https://arxiv.org/abs/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 - GEN 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 T2 - arxiv.org 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 -