ArticleScientific reports2018
Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- The application and development of electron microscopy for three-dimensional reconstruction in life science: a review.Cell and tissue research · 2024Review
- Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts.Chemistry of materials : a publication of the American Chemical Society · 2023Article
- Deep Learning to Analyze Sliding Drops.Langmuir : the ACS journal of surfaces and colloids · 2023Article
- Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives.Nanomaterials (Basel, Switzerland) · 2022Review
- Hierarchical ultrastructure: An overview of what is known about tendons and future perspective for tendon engineering.Bioactive materials · 2022Review
- An Adversarial Learning Approach for Super-Resolution Enhancement Based on AgCl@Ag Nanoparticles in Scanning Electron Microscopy Images.Nanomaterials (Basel, Switzerland) · 2021Article
- Deep learning-based estimation of Flory-Huggins parameter of A-B block copolymers from cross-sectional images of phase-separated structures.Scientific reports · 2021Article
- Electron Microscopy Techniques for Investigating Structure and Composition of Hair-Cell Stereociliary Bundles.Frontiers in cell and developmental biology · 2021Article
- Study of Commodity VR for Computational Material Sciences.ACS omega · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image. Here, we propose a new approach based on an image-processing or deep-learning-based method for super-resolution of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. The deep-learning-based method learns from high-resolution sub-images obtained via SEM and recovers low-resolution sub-images parallel to the FIB milling direction. The 3D morphologies of polymeric nano-composites are used as test images, which are subjected to the deep-learning-based method as well as conventional methods. We find that the former yields superior restoration, particularly as the asymmetric resolution is increased. Our super-resolution approach for images having asymmetric resolution enables observation time reduction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.