Image Reconstruction in Dynamic Inverse Problems with Temporal Models

Hauptmann, Andreas; Öktem, Ozan; Schönlieb, Carola-Bibiane

doi:10.1007/978-3-030-03009-4_83-1

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…In this case, the data may be more effectively processed by exploiting time‐spatial correlations via “low‐rank” methods [ 177 ] or via temporal models. [ 178 ] As a special case of the latter, one can enforce the conformance of the image reconstructions to some fixed physical model, for example, a model for dendrite growth.…”

Section: Emerging Trendsmentioning

confidence: 99%

Multi‐Dimensional Characterization of Battery Materials

Ziesche

Heenan

Kumari

et al. 2023

Advanced Energy Materials

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Demand for low carbon energy storage has highlighted the importance of imaging techniques for the characterization of electrode microstructures to determine key parameters associated with battery manufacture, operation, degradation, and failure both for next generation lithium and other novel battery systems. Here, recent progress and literature highlights from magnetic resonance, neutron, X‐ray, focused ion beam, scanning and transmission electron microscopy are summarized. Two major trends are identified: First, the use of multi‐modal microscopy in a correlative fashion, providing contrast modes spanning length‐ and time‐scales, and second, the application of machine learning to guide data collection and analysis, recognizing the role of these tools in evaluating large data streams from increasingly sophisticated imaging experiments.

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Section: Emerging Trendsmentioning

confidence: 99%

Multi‐Dimensional Characterization of Battery Materials

Ziesche

Heenan

Kumari

et al. 2023

Advanced Energy Materials

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show abstract

“…Here we distinguish between motion compensation [6], [7], that aims to reconstruct a reference state along with motion information, and dynamic reconstructions that provide a reconstruction of the target in the 2D+1 space-time cube [8], [9]. We refer to [10] for a recent overview. Naturally, only methods of the latter, full dynamic type are relevant here.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Springer¹,

Glielmo²,

Senchukova³

et al. 2022

Preprint

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In industrial applications it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality is evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement of reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.

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“…Here we distinguish between motion compensation [20,21], which aims to reconstruct a reference state along with motion information, and dynamic reconstructions that provide a reconstruction of the target in the 2D+1 space-time cube [9,31]. We refer to [23] for a recent overview. Naturally, only methods of the latter, full dynamic type are relevant here.…”

mentioning

confidence: 99%

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Springer,

Glielmo,

Senchukova

et al. 2023

AMMC

Self Cite

View full text Add to dashboard Cite

In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality are evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement in reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.

show abstract

Image Reconstruction in Dynamic Inverse Problems with Temporal Models

Cited by 7 publications

References 51 publications

Multi‐Dimensional Characterization of Battery Materials

Multi‐Dimensional Characterization of Battery Materials

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

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