Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging

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 iter...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 22; číslo 15; s. 5533
Hlavní autoři:	Hauffen, Jan Christian, Kästner, Linh, Ahmadi, Samim, Jung, Peter, Caire, Giuseppe, Ziegler, Mathias
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 25.07.2022 MDPI
Témata:	3-D printers active thermal imaging Algorithms Applied mathematics block-sparsity Cameras deep unfolding defect reconstruction Defects Heat detection Inverse problems Investigations iterative shrinkage thresholding algorithm laser thermography Lasers Mathematical models Nondestructive testing Optimization techniques Regularization methods Sparsity Thermography
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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. 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.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.
Author	Ahmadi, Samim Kästner, Linh Jung, Peter Caire, Giuseppe Ziegler, Mathias Hauffen, Jan Christian
AuthorAffiliation	1 Communication and Information Theory, Berlin Institute of Technology, 10623 Berlin, Germany; caire@tu-berlin.de 3 Department of Non-Destructive Testing, Bundesanstalt für Materialforschung und -Prüfung, 12489 Berlin, Germany; samim_193@hotmail.de (S.A.); mathias.ziegler@bam.de (M.Z.) 2 Industry Grade Networks and Clouds, Faculty of Electrical Engineering, and Computer Science, Berlin Institute of Technology, 10623 Berlin, Germany; d.kaestner@tu-berlin.de
AuthorAffiliation_xml	– name: 1 Communication and Information Theory, Berlin Institute of Technology, 10623 Berlin, Germany; caire@tu-berlin.de – name: 3 Department of Non-Destructive Testing, Bundesanstalt für Materialforschung und -Prüfung, 12489 Berlin, Germany; samim_193@hotmail.de (S.A.); mathias.ziegler@bam.de (M.Z.) – name: 2 Industry Grade Networks and Clouds, Faculty of Electrical Engineering, and Computer Science, Berlin Institute of Technology, 10623 Berlin, Germany; d.kaestner@tu-berlin.de
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SubjectTerms	3-D printers active thermal imaging Algorithms Applied mathematics block-sparsity Cameras deep unfolding defect reconstruction Defects Heat detection Inverse problems Investigations iterative shrinkage thresholding algorithm laser thermography Lasers Mathematical models Nondestructive testing Optimization techniques Regularization methods Sparsity Thermography
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Title	Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
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