Radon Transform Methods for Radar, Seismic, and Medical Imaging
This community develops mathematical algorithms for reconstructing images and detecting targets from raw sensor data, primarily using the Radon transform and its variants.
The work centers on applying the Radon transform, Fourier transform, and generalized Radon variants to solve inverse problems in imaging. Key applications include target detection and maneuvering target imaging in aperture radar, seismic data processing and shear wave analysis, and computed tomography reconstruction. A significant portion of the research also focuses on image encryption algorithms, often utilizing chaotic maps and DNA coding for secure data transmission. These methods are frequently combined with deep learning and neural networks to enhance detection accuracy and reconstruction speed. The mathematical foundations often involve Banach spaces, optimal transport, and transmutation operators to handle the complex geometry of the data.
The largest share of this community's output is found in radon research, accounting for 9.3% of all radon-related papers tracked, which corresponds to 1,902 papers within this group.
The community comprises 2,977 papers, with the highest publication volume in Inverse Problems, Optics & Laser Technology, and Geophysics.
Recent work continues to apply Radon transform-based formulas for reconstructing acoustic sources and classifying flying targets, alongside new schemes for chaotic image encryption and physics-informed neural networks for materials modeling.