{
  "abstract": "Digital Rock Physics (DRP) requires high-fidelity pore-scale imaging, yet micro-computed tomography (μ-CT) trades spatial resolution against field of view, and standard super-resolution (SR) often fails to preserve transport-controlling pore topology. We propose Vision-LSTM and State-Space Super-Resolution (ViSSR), a physics-informed hybrid residual SR network that couples Vision-LSTM (ViL) for local anisotropic texture with a Mamba-based 2D selective state-space module (SS2D) for linear-complexity global context, and restores high-frequency structure via scale-conditioned residuals under an explicit partial-volume and PSF degradation model. Because reconstruction errors propagate into permeability and flow analysis, we report the pixel metrics PSNR, SSIM, and LPIPS together with topology-aware scores comprising pore-size-distribution earth mover’s distance (PSD-EMD; one-dimensional Wasserstein-1), porosity, connectivity, spanning, and fractal dimension. On DeepRock-SR, ViSSR attains 32.08 dB PSNR, 0.746 SSIM, and 0.04 PSD-EMD at ×4, and preserves pore topology across sandstone, carbonate, and coal where perceptual baselines hallucinate or oversmooth. Zero-shot slice-stacked reconstruction on independently acquired dual-resolution carbonates from MRCCM, project DRP-362, further yields volumetric transport gains over bicubic upsampling under scanner-realized ×4 degradation.",
  "abstract_shown": true,
  "affiliations": [
    "School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing 163318, China",
    "Department of Geology, Northeast Petroleum University, Qinhuangdao 066004, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yanchang Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Shuyi Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Weiyi Zhao"
    },
    {
      "affs": [
        1
      ],
      "name": "Hanning Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yiting Liao"
    },
    {
      "affs": [
        0
      ],
      "name": "Yifan Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bg4s3f50v0b6z.bib",
  "cited_by": 0,
  "doi": "10.3390/app162010106",
  "fulltext_url": "https://www.mdpi.com/2076-3417/16/20/10106",
  "id": "bg4s3f50v0b6z",
  "issns": [
    "2076-3417"
  ],
  "landing_url": "https://doi.org/10.3390/app162010106",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC1-999"
    ],
    [
      "lcc",
      "QD1-999"
    ],
    [
      "lcc",
      "QH301-705.5"
    ],
    [
      "lcc",
      "T"
    ],
    [
      "lcc",
      "TA1-2040"
    ],
    [
      "openalex",
      "1707|Advanced Image Processing Techniques"
    ]
  ],
  "openalex": "W7221523798",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/bg4s3f50v0b6z.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:21:21Z",
      "native_id": "05fc7aacdf4447b98b409fc94160a7e6",
      "source": "doaj",
      "url": "https://doi.org/10.3390/app162010106"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "lcc:QH301-705.5",
      "subfield": "general"
    },
    {
      "field": "chemistry",
      "origin": "lcc:QD1-999",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "ViSSR: Physics-Informed Hybrid Vision-LSTM/State-Space Super-Resolution for Digital Rock Microstructure Analysis",
  "title_text": "ViSSR: Physics-Informed Hybrid Vision-LSTM/State-Space Super-Resolution for Digital Rock Microstructure Analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:21Z",
  "url": "https://research.draconitic.com/paper/bg4s3f50v0b6z",
  "venue": "Applied Sciences",
  "versions": []
}