arXiv (All)
open access
Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction
Reconstructing a three-dimensional hyperspectral cube from a two-dimensional compressed measurement is a severely ill-posed inverse problem. Existing deep unfolding networks (DUNs) retain fidelity to the imaging model, but regression-trained denoisers can suppress spatial detail and smooth spectral
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
- Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications#1083998
- AXS-Net: Interpretable Deep Unfolding for Hyperspectral Image Denoising via Spectral Basis Unmixing and Structured Noise Refinement#986415
- Deep learning methods for accelerated MRI reconstruction: Optimization-inspired unrolled networks and beyond#206998
- GenNVS: Geometry-enhanced Novel View Synthesis via Disentangled 3D Prior#1131665
- Data Sheet 1_Deep learning-based 3D gravity inversion: a comparative analysis of CNN architectures for density estimation.pdf#635009
Record · ID 984900
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