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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.06082 |
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| _version_ | 1866911142429851648 |
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| author | Mishra, Anuraag Gilch, Andrea Zubiri, Benjamin Apeleo Rolfes, Jan Liers, Frauke |
| author_facet | Mishra, Anuraag Gilch, Andrea Zubiri, Benjamin Apeleo Rolfes, Jan Liers, Frauke |
| contents | In this work, we develop a novel technique for reconstructing images from projection-based nano- and microtomography. Our contribution focuses on enhancing reconstruction quality, particularly for specimen composed of homogeneous material phases connected by sharp edges. This is accomplished by training a neural network to identify edges within subpictures. The trained network is then integrated into a mathematical optimization model, to reduce artifacts from previous reconstructions. To this end, the optimization approach favors solutions according to the learned predictions, however may also determine alternative solutions if these are strongly supported by the raw data. Hence, our technique successfully incorporates knowledge about the homogeneity and presence of sharp edges in the sample and thereby eliminates blurriness. Our results on experimental datasets show significant enhancements in interface sharpness and material homogeneity compared to benchmark algorithms. Thus, our technique produces high-quality reconstructions, showcasing its potential for advancing tomographic imaging techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06082 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Quality Tomographic Image Reconstruction Integrating Neural Networks and Mathematical Optimization Mishra, Anuraag Gilch, Andrea Zubiri, Benjamin Apeleo Rolfes, Jan Liers, Frauke Computer Vision and Pattern Recognition Materials Science 90C20, 94A08, 68U10 In this work, we develop a novel technique for reconstructing images from projection-based nano- and microtomography. Our contribution focuses on enhancing reconstruction quality, particularly for specimen composed of homogeneous material phases connected by sharp edges. This is accomplished by training a neural network to identify edges within subpictures. The trained network is then integrated into a mathematical optimization model, to reduce artifacts from previous reconstructions. To this end, the optimization approach favors solutions according to the learned predictions, however may also determine alternative solutions if these are strongly supported by the raw data. Hence, our technique successfully incorporates knowledge about the homogeneity and presence of sharp edges in the sample and thereby eliminates blurriness. Our results on experimental datasets show significant enhancements in interface sharpness and material homogeneity compared to benchmark algorithms. Thus, our technique produces high-quality reconstructions, showcasing its potential for advancing tomographic imaging techniques. |
| title | High-Quality Tomographic Image Reconstruction Integrating Neural Networks and Mathematical Optimization |
| topic | Computer Vision and Pattern Recognition Materials Science 90C20, 94A08, 68U10 |
| url | https://arxiv.org/abs/2509.06082 |