Blind structured illumination as excitation for super-resolution photothermal radiometry

Burgholzer, Peter; Berer, Thomas; Ziegler, Mathias; Thiel, Erik; Ahmadi, Samim; Gruber, Jürgen; Mayr, G.; Hendorfer, G.

doi:10.1080/17686733.2019.1655247

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…A new laser-based heat source is the vertical-cavity surface-emitting laser (VCSEL) array, which has been characterized for thermographic non-destructive testing and validated for use in high-frequency lock-in thermography [14]. Furthermore, this source has proven to be suitable for novel interference methods with thermal waves [15] and for super resolution thermography [16]. The mentioned laser source is used here together with PuC and pulse excitation for the comparative analysis of CFRP samples.…”

Section: Active Thermography Using High-power Laser Arraysmentioning

confidence: 99%

Temporal shaping and pulse-compression in thermography using laser heating

Hirsch¹,

Malekmohammadi²,

Ahmadi³

et al. 2020

Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography

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Section: Active Thermography Using High-power Laser Arraysmentioning

confidence: 99%

Temporal shaping and pulse-compression in thermography using laser heating

Hirsch¹,

Malekmohammadi²,

Ahmadi³

et al. 2020

Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography

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“…The resulting high sensitivity was used to detect vertical crack-like defects below the surface, which can only sometimes be found with classical active thermography. In another approach [12], closely adjacent defects could be separated by multiple measurements with varying illumination structures. We present the latest results of this technology obtained with lasers, i.e.…”

Section: Spatial and Temporal Shaping Of Diffuse Thermal Wave Fields Using High-power Lasersmentioning

confidence: 99%

Spatial and temporal shaping of diffuse thermal wave fields using high-power lasers

Ziegler¹,

Ahmadi²,

Hirsch³

et al. 2020

Proceedings of the 2020 International Conference on Quantitative InfraRed Thermography

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Although thermography is suitable for a wide range of inhomogeneities and materials, the fundamental limitation is the diffuse nature of thermal waves and the need to measure their effect radiometrically at the sample surface. The crucial difference between diffuse thermal waves and propagating waves is the rapid degradation of spatial resolution with increasing depth. A promising approach to improve the spatial resolution and thus detection sensitivity and reconstruction quality lies in shaping of these diffuse thermal wave fields. We present the latest results of this technology obtained with lasers, i.e. spatially and temporally structureable heating sources and modern numerical methods.

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“…Especially for multiple-measurement-vector (MMV) problems, group LASSO as a block-regularization method can be applied to find an accurate solution [ 21 , 22 ]. Block-sparse recovery has been extensively studied with performance guarantees, also in fields of applied mathematics [ 23 , 24 , 25 ] and has been recently applied to active thermal imaging for defect detection in nondestructive testing [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. Using multiple and different blind illumination patterns for heating in active thermal imaging results in an MMV problem.…”

Section: Introductionmentioning

confidence: 99%

“…Using multiple and different blind illumination patterns for heating in active thermal imaging results in an MMV problem. Blind illumination patterns assume that the exact position of illumination is unknown in industrial applications [ 29 ]. This occurs, for example, when the heat source or the specimen is held by a robot that has a certain amount of positional noise.…”

Section: Introductionmentioning

confidence: 99%

Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging

Ahmadi

Hauffen

Kästner

et al. 2022

Sensors

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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.

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Blind structured illumination as excitation for super-resolution photothermal radiometry

Cited by 12 publications

References 14 publications

Temporal shaping and pulse-compression in thermography using laser heating

Temporal shaping and pulse-compression in thermography using laser heating

Spatial and temporal shaping of diffuse thermal wave fields using high-power lasers

Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging

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