TopoRSNet: A Unique Approach to Maintaining Topological Features for Digital Rock Image Rescaling With Minimal Quality Degradation Article Swipe
Kunning Tang
,
Yufu Niu
,
Ying Da Wang
,
Vanessa Robins
,
Peyman Mostaghimi
,
Mark Lindsay
,
Ryan T. Armstrong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1029/2024jh000557
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1029/2024jh000557
The composition, microstructure, and physical behavior of rock at the core‐to‐pore scale are commonly visualized and characterized using 3D X‐ray microcomputed tomography (micro‐CT). However, a key geophysical problem in micro‐CT rock characterization is how to efficiently handle and process large numbers of high‐resolution 3D rock images, and how to preserve multiscale features for characterization at coarse resolution. Typically, a single high‐resolution 3D micro‐CT image can be extremely large with more than voxels, and in the case of dynamic scans, hundreds of such large images will be generated, posing challenges in storage, downstream analysis, modeling, and their application to deep learning. Existing image rescaling methods face challenges such as information loss during down‐sampling and limited feature recovery during up‐sampling. Herein, we present TopoRSNet, an image rescaling network designed to adjust micro‐CT images to an optimal size and resolution while preserving global and local representative features. Feature preservation is ensured by introducing three feature‐based loss functions: adversarial loss, a new feature consistency loss, and a novel persistent homology loss (PH loss). Combined with a pixel consistency loss, we assure the preservation of both pixels and features during rescaling. The efficacy of TopoRSNet is validated on two common geological rocks, and the results are compared to other rescaling methods. This method enables scalable analysis of multiscale rock features, allowing for broader integration of 3D imaging in geoscientific modeling, simulation, and machine learning workflows.
Related Topics
Concepts
Degradation (telecommunications)
Image (mathematics)
Quality (philosophy)
Digital image analysis
Digital image
Image quality
Computer science
Topology (electrical circuits)
Geology
Mathematics
Artificial intelligence
Computer vision
Image processing
Combinatorics
Philosophy
Epistemology
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2024jh000557
- OA Status
- diamond
- Cited By
- 1
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411646640
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411646640Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2024jh000557Digital Object Identifier
- Title
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TopoRSNet: A Unique Approach to Maintaining Topological Features for Digital Rock Image Rescaling With Minimal Quality DegradationWork 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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2025-06-01Full publication date if available
- Authors
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Kunning Tang, Yufu Niu, Ying Da Wang, Vanessa Robins, Peyman Mostaghimi, Mark Lindsay, Ryan T. ArmstrongList of authors in order
- Landing page
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https://doi.org/10.1029/2024jh000557Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1029/2024jh000557Direct OA link when available
- Concepts
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Degradation (telecommunications), Image (mathematics), Quality (philosophy), Digital image analysis, Digital image, Image quality, Computer science, Topology (electrical circuits), Geology, Mathematics, Artificial intelligence, Computer vision, Image processing, Combinatorics, Philosophy, Epistemology, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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72Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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