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 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/.