arXiv (All)
open access
Data Completion for Electrical Impedance Tomography by Conditional Diffusion Models
Data scarcity is a fundamental barrier in Electrical Impedance Tomography (EIT), as undersampled Dirichlet-to-Neumann (DtN) measurements can substantially degrade conductivity reconstructions. We address this bottleneck by completing partially observed DtN measurements using a diffusion based genera
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Electrical impedance tomography for anisotropic media: A machine learning approach to classify inclusions#186684
- Ultrasound-guided structural fusion enhances the interpretability of electrical impedance tomography for layered tissue imaging.#785530
- Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography#635618
- Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography#633269
- Rethinking the design space of Vanilla Pixel Diffusion Transformers#1388221
Record · ID 1389193
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive