{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T02:39:32Z","timestamp":1782441572125,"version":"3.54.5"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008917","name":"Colorado School of Mines","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2302764"],"award-info":[{"award-number":["2302764"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["EAR # 2401621"],"award-info":[{"award-number":["EAR # 2401621"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.asoc.2025.114463","type":"journal-article","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T16:19:45Z","timestamp":1766247585000},"page":"114463","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Detail-rich pore-scale reconstruction with conditional latent diffusion"],"prefix":"10.1016","volume":"189","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5548-4805","authenticated-orcid":false,"given":"Pejman","family":"Tahmasebi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2025.114463_bib1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.chemgeo.2016.02.030","article-title":"Reservoir condition imaging of reactive transport in heterogeneous carbonates using fast synchrotron tomography - effect of initial pore structure and flow conditions","volume":"428","author":"Menke","year":"2016","journal-title":"Chem. Geol."},{"key":"10.1016\/j.asoc.2025.114463_bib2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.advwatres.2014.02.014","article-title":"Pore-scale contact angle measurements at reservoir conditions using X-ray microtomography","volume":"68","author":"Andrew","year":"2014","journal-title":"Adv. Water Resour."},{"key":"10.1016\/j.asoc.2025.114463_bib3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.petrol.2016.12.031","article-title":"Pore-scale simulation of flow of CO2 and brine in reconstructed and actual 3D rock cores","volume":"155","author":"Tahmasebi","year":"2017","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.asoc.2025.114463_bib4","first-page":"0","article-title":"A robust NMR method to measure porosity of low porosity rocks","author":"Yan","year":"2018","journal-title":"Microporous Mesoporous Mater."},{"key":"10.1016\/j.asoc.2025.114463_bib5","first-page":"548","article-title":"NMR imaging of gas imbibed into porous ceramic","volume":"95","author":"Lizak","year":"1991","journal-title":"J. Magn. Reson"},{"key":"10.1016\/j.asoc.2025.114463_bib6","unstructured":"Y. Keehm, A. Nur, Computational rock physics: Transport properties in porous media and applications, Stanford University, 2003. \u3008https:\/\/srb.stanford.edu\/computational-rock-physics-transport-properties-porous-media-and-applications\u3009 (accessed May 16, 2020)."},{"key":"10.1016\/j.asoc.2025.114463_bib7","article-title":"Striving to translate shale physics across ten orders of magnitude: what have we learned?","volume":"223","author":"Mehmani","year":"2021","journal-title":"EarthSci. Rev."},{"key":"10.1016\/j.asoc.2025.114463_bib8","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.advwatres.2015.06.010","article-title":"Assessing the utility of FIB-SEM images for shale digital rock physics","volume":"95","author":"Kelly","year":"2016","journal-title":"Adv. Water Resour."},{"key":"10.1016\/j.asoc.2025.114463_bib9","doi-asserted-by":"crossref","first-page":"15672","DOI":"10.1021\/acs.energyfuels.0c03397","article-title":"Digital rock techniques to study shale permeability: a mini-review","volume":"34","author":"Tahmasebi","year":"2020","journal-title":"Energy Fuels"},{"key":"10.1016\/j.asoc.2025.114463_bib10","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-97915-y","article-title":"Tunable X-ray dark-field imaging for sub-resolution feature size quantification in porous media","volume":"11","author":"Blykers","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2025.114463_bib11","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.neunet.2019.07.009","article-title":"Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm","volume":"118","author":"Kamrava","year":"2019","journal-title":"Neural Netw."},{"key":"10.1016\/j.asoc.2025.114463_bib12","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.petrol.2013.10.005","article-title":"The construction of carbonate digital rock with hybrid superposition method","volume":"110","author":"Yao","year":"2013","journal-title":"J. Pet. Sci. Eng."},{"key":"10.1016\/j.asoc.2025.114463_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.fuel.2023.128753","article-title":"An end-to-end approach to predict physical properties of heterogeneous porous media: Coupling deep learning and physics-based features","volume":"352","author":"Wu","year":"2023","journal-title":"Fuel"},{"key":"10.1016\/j.asoc.2025.114463_bib14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.marpetgeo.2019.06.006","article-title":"Multiscale modeling of shale samples based on low- and high-resolution images","volume":"109","author":"Wu","year":"2019","journal-title":"Mar. Pet. Geol."},{"key":"10.1016\/j.asoc.2025.114463_bib15","doi-asserted-by":"crossref","first-page":"6911","DOI":"10.1029\/2019WR025219","article-title":"Multiscale digital porous rock reconstruction using template matching","volume":"55","author":"Lin","year":"2019","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.asoc.2025.114463_bib16","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.geoderma.2017.10.055","article-title":"Enhancing image resolution of soils by stochastic multiscale image fusion","volume":"314","author":"Karsanina","year":"2018","journal-title":"Geoderma"},{"key":"10.1016\/j.asoc.2025.114463_bib17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.fuel.2017.12.107","article-title":"Nanoscale and multiresolution models for shale samples","volume":"217","author":"Tahmasebi","year":"2018","journal-title":"Fuel"},{"key":"10.1016\/j.asoc.2025.114463_bib18","doi-asserted-by":"crossref","DOI":"10.1029\/2019WR026052","article-title":"Boosting resolution and recovering texture of 2D and 3D Micro-CT images with deep learning","volume":"56","author":"Wang","year":"2020","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.asoc.2025.114463_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-30949-6","article-title":"Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy","volume":"13","author":"Park","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.asoc.2025.114463_bib20","article-title":"Multiscale fusion of digital rock images based on deep generative adversarial networks","volume":"49","author":"Liu","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.asoc.2025.114463_bib21","first-page":"2242","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"10.1016\/j.asoc.2025.114463_bib22","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.106.055301","article-title":"End-to-end three-dimensional designing of complex disordered materials from limited data using machine learning","volume":"106","author":"Kamrava","year":"2022","journal-title":"Phys. Rev. E"},{"key":"10.1016\/j.asoc.2025.114463_bib23","doi-asserted-by":"crossref","first-page":"120704","DOI":"10.1016\/j.actamat.2024.120704","article-title":"Inverse design of microstructures using conditional continuous normalizing flows","volume":"285","author":"Mirzaee","year":"2025","journal-title":"Acta Mater"},{"key":"10.1016\/j.asoc.2025.114463_bib24","doi-asserted-by":"crossref","DOI":"10.1103\/y1h6-15cc","article-title":"Latent diffusion modeling of porous media informed by spatial statistics","author":"Tahmasebi","year":"2025","journal-title":"Phys. Rev. E"},{"key":"10.1016\/j.asoc.2025.114463_bib25","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s11242-025-02243-8","article-title":"Learning to fill: reconstructing scientific microstructure images using probabilistic networks","volume":"152","author":"Tahmasebi","year":"2025","journal-title":"Transp. Porous Media 2025 15212"},{"key":"10.1016\/j.asoc.2025.114463_bib26","article-title":"Ensemble-guided machine learning for feature-constrained porous media","author":"Tahmasebi","year":"2025","journal-title":"Comput. Mater. Sci."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494625017764?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494625017764?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:43:27Z","timestamp":1770857007000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494625017764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":26,"alternative-id":["S1568494625017764"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2025.114463","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Detail-rich pore-scale reconstruction with conditional latent diffusion","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2025.114463","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"114463"}}