Deep learning based optical image super-resolution via generative diffusion models for layerwise in-situ LPBF monitoring Article Swipe
Francis Ogoke
,
Shilpa Suresh
,
Jesse Adamczyk
,
Dan Bolintineanu
,
Anthony Garland
,
Michael Heiden
,
Amir Barati Farimani
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.addma.2025.104790
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.addma.2025.104790
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.addma.2025.104790
- OA Status
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- References
- 78
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410551960Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.addma.2025.104790Digital Object Identifier
- Title
-
Deep learning based optical image super-resolution via generative diffusion models for layerwise in-situ LPBF monitoringWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-21Full publication date if available
- Authors
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Francis Ogoke, Shilpa Suresh, Jesse Adamczyk, Dan Bolintineanu, Anthony Garland, Michael Heiden, Amir Barati FarimaniList of authors in order
- Landing page
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https://doi.org/10.1016/j.addma.2025.104790Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/2567550Direct OA link when available
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Materials science, In situ, Diffusion, Generative grammar, Image (mathematics), Superresolution, Generative model, Resolution (logic), High resolution, Artificial intelligence, Optics, Biological system, Remote sensing, Computer science, Thermodynamics, Physics, Meteorology, Biology, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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78Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 78 |
| abstract_inverted_index | |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.17341227 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |