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
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Cites_doi	10.1080/17686733.2019.1686896 10.1109/TIM.2022.3154803 10.1137/S0036139994277828 10.1109/TIT.2011.2143890 10.1109/TSP.2011.2105478 10.1109/MSP.2007.4286571 10.1109/TII.2021.3054411 10.1201/9781439895214 10.1002/9783527693306 10.1080/17686733.2019.1655247 10.1109/IEMBS.2005.1616506 10.1109/CVPR.2018.00291 10.3390/s18020609 10.1038/s41598-020-64796-6 10.1364/OPTICA.4.000017 10.1080/17686733.2020.1829889 10.1109/MMAR.2013.6669964 10.1137/080716542 10.1109/TNNLS.2020.2980383 10.1109/TIT.2006.871582 10.1016/j.mser.2008.11.001 10.1016/j.optlaseng.2020.106279 10.1109/TSP.2017.2708040 10.1038/s41598-020-77979-y 10.1088/0266-5611/21/1/012 10.1016/j.ndteint.2020.102228 10.1016/j.bbe.2018.03.009 10.1109/TSP.2012.2196694 10.1063/1.4995410 10.1109/ICASSP.2009.4960226 10.3390/met10010103 10.1002/admt.201800717 10.1109/ACCESS.2020.2978237 10.1016/j.ijheatmasstransfer.2018.11.089 10.1007/s10114-013-1564-y
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References	Donoho (ref_14) 2006; 52 Lv (ref_21) 2011; 59 Lin (ref_24) 2013; 29 Ahmadi (ref_32) 2020; 10 Beck (ref_16) 2009; 2 Murray (ref_27) 2017; 4 Metz (ref_36) 2021; 18 Ahmadi (ref_31) 2020; 134 ref_34 Hung (ref_9) 2009; 64 Ahmadi (ref_26) 2021; 17 Samadi (ref_7) 2020; 10 ref_10 Kim (ref_19) 2020; 8 Recinella (ref_5) 2019; 131 Baraniuk (ref_15) 2007; 24 Altenburg (ref_35) 2020; 19 Bryan (ref_11) 1996; 56 Burgholzer (ref_29) 2020; 7 ref_18 ref_17 Wang (ref_37) 2021; 32 ref_39 ref_38 Wang (ref_12) 2004; 21 Boufounos (ref_25) 2011; 57 Strzelecki (ref_4) 2018; 38 ref_22 ref_43 ref_20 ref_41 ref_1 ref_3 Burgholzer (ref_28) 2017; 111 Elhamifar (ref_23) 2012; 60 Seppala (ref_33) 2016; 12 Ahmadi (ref_40) 2022; 71 Ahmadi (ref_30) 2020; 111 Borgerding (ref_42) 2017; 65 ref_8 Kukreja (ref_13) 2006; 39 Ortega (ref_2) 2019; 4 ref_6
References_xml	– volume: 39 start-page: 814 year: 2006 ident: ref_13 article-title: A least absolute shrinkage and selection operator (LASSO) for nonlinear system identification publication-title: IFAC Proc. – volume: 18 start-page: 50 year: 2021 ident: ref_36 article-title: Active thermography for quality assurance of 3D-printed polymer structures publication-title: Quant. Infrared Thermogr. J. doi: 10.1080/17686733.2019.1686896 – volume: 71 start-page: 4502509 year: 2022 ident: ref_40 article-title: Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2022.3154803 – volume: 56 start-page: 715 year: 1996 ident: ref_11 article-title: An inverse problem in thermal imaging publication-title: SIAM J. Appl. Math. doi: 10.1137/S0036139994277828 – volume: 57 start-page: 3864 year: 2011 ident: ref_25 article-title: Sparse recovery from combined fusion frame measurements publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2011.2143890 – volume: 59 start-page: 1371 year: 2011 ident: ref_21 article-title: The Group Lasso for Stable Recovery of Block-Sparse Signal Representations publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2011.2105478 – volume: 24 start-page: 187 year: 2007 ident: ref_15 article-title: Compressive Sensing publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2007.4286571 – volume: 17 start-page: 7368 year: 2021 ident: ref_26 article-title: Multidimensional Reconstruction of Internal Defects in Additively Manufactured Steel Using Photothermal Super Resolution Combined With Virtual Wave-Based Image Processing publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2021.3054411 – ident: ref_10 doi: 10.1201/9781439895214 – ident: ref_1 doi: 10.1002/9783527693306 – volume: 7 start-page: 268 year: 2020 ident: ref_29 article-title: Blind structured illumination as excitation for super-resolution photothermal radiometry publication-title: Quant. Infrared Thermogr. J. doi: 10.1080/17686733.2019.1655247 – ident: ref_6 doi: 10.1109/IEMBS.2005.1616506 – ident: ref_18 – ident: ref_39 doi: 10.1109/CVPR.2018.00291 – ident: ref_3 doi: 10.3390/s18020609 – volume: 10 start-page: 7857 year: 2020 ident: ref_7 article-title: Low-cost active thermography using cellphone infrared cameras: From early detection of dental caries to quantification of THC in oral fluid publication-title: Sci. Rep. doi: 10.1038/s41598-020-64796-6 – volume: 4 start-page: 17 year: 2017 ident: ref_27 article-title: Super-resolution photoacoustic microscopy using blind structured illumination publication-title: Optica doi: 10.1364/OPTICA.4.000017 – volume: 19 start-page: 97 year: 2020 ident: ref_35 article-title: In-situ monitoring of a laser metal deposition (LMD) process: Comparison of MWIR, SWIR and high-speed NIR thermography publication-title: Quant. Infrared Thermogr. J. doi: 10.1080/17686733.2020.1829889 – ident: ref_41 doi: 10.1109/MMAR.2013.6669964 – volume: 2 start-page: 183 year: 2009 ident: ref_16 article-title: A fast iterative shrinkage-thresholding algorithm for linear inverse problems publication-title: SIAM J. Imaging Sci. doi: 10.1137/080716542 – volume: 32 start-page: 1110 year: 2021 ident: ref_37 article-title: Feature Selection Using a Neural Network With Group Lasso Regularization and Controlled Redundancy publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2020.2980383 – ident: ref_8 – volume: 12 start-page: 71 year: 2016 ident: ref_33 article-title: Infrared thermography of welding zones produced by polymer extrusion additive manufacturing publication-title: Addit. Manuf. – volume: 52 start-page: 1289 year: 2006 ident: ref_14 article-title: Compressed Sensing publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2006.871582 – volume: 64 start-page: 73 year: 2009 ident: ref_9 article-title: Review and comparison of shearography and active thermography for nondestructive evaluation publication-title: Mater. Sci. Eng. R Rep. doi: 10.1016/j.mser.2008.11.001 – volume: 134 start-page: 106279 year: 2020 ident: ref_31 article-title: Super resolution laser line scanning thermography publication-title: Opt. Lasers Eng. doi: 10.1016/j.optlaseng.2020.106279 – volume: 65 start-page: 4293 year: 2017 ident: ref_42 article-title: AMP-Inspired Deep Networks for Sparse Linear Inverse Problems publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2017.2708040 – volume: 10 start-page: 22357 year: 2020 ident: ref_32 article-title: Laser excited super resolution thermal imaging for nondestructive inspection of internal defects publication-title: Sci. Rep. doi: 10.1038/s41598-020-77979-y – volume: 21 start-page: 183 year: 2004 ident: ref_12 article-title: Hiearchical Bayesian models for inverse problems in heat conduction publication-title: Inverse Probl. doi: 10.1088/0266-5611/21/1/012 – volume: 111 start-page: 102228 year: 2020 ident: ref_30 article-title: Photothermal super resolution imaging: A comparison of different thermographic reconstruction techniques publication-title: NDT E Int. J. doi: 10.1016/j.ndteint.2020.102228 – volume: 38 start-page: 602 year: 2018 ident: ref_4 article-title: Thermal modelling and screening method for skin pathologies using active thermography publication-title: Biocybern. Biomed. Eng. doi: 10.1016/j.bbe.2018.03.009 – volume: 60 start-page: 4094 year: 2012 ident: ref_23 article-title: Block-sparse recovery via convex optimization publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2196694 – volume: 111 start-page: 031908 year: 2017 ident: ref_28 article-title: Super-resolution thermographic imaging using blind structured illumination publication-title: Appl. Phys. Lett. doi: 10.1063/1.4995410 – ident: ref_38 – ident: ref_17 – ident: ref_22 doi: 10.1109/ICASSP.2009.4960226 – ident: ref_34 doi: 10.3390/met10010103 – ident: ref_43 – volume: 4 start-page: 1800717 year: 2019 ident: ref_2 article-title: Active mixing of disparate inks for multimaterial 3D printing publication-title: Adv. Mater. Technol. doi: 10.1002/admt.201800717 – volume: 8 start-page: 45874 year: 2020 ident: ref_19 article-title: Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2978237 – ident: ref_20 – volume: 131 start-page: 558 year: 2019 ident: ref_5 article-title: Technology, application and potential of dynamic breast thermography for the detection of breast cancer publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2018.11.089 – volume: 29 start-page: 1401 year: 2013 ident: ref_24 article-title: Block sparse recovery via mixed l 2/l 1 minimization publication-title: Acta Math. Sin. Engl. Ser. doi: 10.1007/s10114-013-1564-y
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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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