EISPY2D: An Open-Source Python Library for the Development and Comparison of Algorithms in Two-Dimensional Electromagnetic Inverse Scattering Problems

Microwave Imaging is a key technique for reconstructing the electrical properties of inaccessible media, relying on algorithms to solve the associated Electromagnetic Inverse Scattering Problem. To support the assessment of recent developments in this field, this work introduces an open-source Pytho...

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Veröffentlicht in:IEEE access Jg. 13; S. 92134 - 92154
Hauptverfasser: Costa Batista, Andre, Adriano, Ricardo, Batista, Lucas S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Comparative studies
Comparison of algorithms
Electrical properties
electromagnetic inverse scattering problem
Electromagnetic scattering
Electromagnetics
Image reconstruction
Imaging
Inverse problems
Inverse scattering
Libraries
Microwave FET integrated circuits
Microwave imaging
Microwave integrated circuits
Microwave theory and techniques
Open source software
open-source library
optimization
Performance measurement
Python
Statistical analysis
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
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Zusammenfassung:Microwave Imaging is a key technique for reconstructing the electrical properties of inaccessible media, relying on algorithms to solve the associated Electromagnetic Inverse Scattering Problem. To support the assessment of recent developments in this field, this work introduces an open-source Python library that provides a modular and standardized framework for implementing and evaluating microwave imaging algorithms. The library facilitates the development and comparison of new methods through a structured class system, offering features such as test randomization, performance metrics, and statistical analysis. To the authors' knowledge, this is the first tool designed specifically for benchmarking and comparative studies in microwave imaging algorithms. The paper presents the library's design principles, along with case studies demonstrating its functionality. The code is freely available on GitHub: https://andre-batista.github.io/eispy2d/ .
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3573679