Inverse Problems and Image Reconstruction for Radar, Seismic, and Medical Imaging
This community develops mathematical algorithms to reconstruct images and locate targets from indirect measurements, applying techniques to radar, seismic exploration, and medical diagnostics.
The work centers on solving inverse problems where raw data must be transformed into spatial images. Recurring methods include the Radon transform, Fourier analysis, and compressed sensing, frequently combined with deep learning and neural networks to improve reconstruction quality. Applications span synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) for detecting maneuvering targets, seismic data inversion for subsurface mapping, and computed tomography (CT) or photoacoustic imaging for medical visualization. The research also addresses parameter estimation and feature extraction in coherent integration systems, aiming to enhance resolution and reduce noise in complex signal environments.
The largest share of this community's output is found in Radon research, accounting for 9.8% of all Radon-related papers tracked, with 4,738 papers in this group.
The community comprises 20,677 papers, publishing most frequently in arXiv (Cornell University), Inverse Problems, and Ultrasonic Imaging.
Recent work includes noise-robust ISAR imaging for geostationary targets, matrix-based imaging through dynamic scattering, and stabilized real-time Brillouin microscopy for observing protein condensates in living cells.