Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
This work provides an efficient and universal solution for inverse scattering problems in fields like electromagnetic imaging and medical diagnostics, though it is incremental as it builds on existing contrast source inversion and neural operator methods.
The paper tackled the nonlinear and computationally expensive inverse scattering problem by proposing DeepCSI, a physics-informed deep contrast source inversion framework, which achieved high-precision and robust medium reconstruction across various measurement conditions, outperforming traditional methods.
Inverse scattering problems are critical in electromagnetic imaging and medical diagnostics but are challenged by their nonlinearity and diverse measurement scenarios. This paper proposes a physics-informed deep contrast source inversion framework (DeepCSI) for fast and accurate medium reconstruction across various measurement conditions. Inspired by contrast source inversion (CSI) and neural operator methods, a residual multilayer perceptron (ResMLP) is employed to model current distributions in the region of interest under different transmitter excitations, effectively linearizing the nonlinear inverse scattering problem and significantly reducing the computational cost of traditional full-waveform inversion. By modeling medium parameters as learnable tensors and utilizing a hybrid loss function that integrates state equation loss, data equation loss, and total variation regularization, DeepCSI establishes a fully differentiable framework for joint optimization of network parameters and medium properties. Compared with conventional methods, DeepCSI offers advantages in terms of simplicity and universal modeling capabilities for diverse measurement scenarios, including phase-less and multi-frequency observation. Simulations and experiments demonstrate that DeepCSI achieves high-precision, robust reconstruction under full-data, phaseless data, and multifrequency conditions, outperforming traditional CSI methods and providing an efficient and universal solution for complex inverse scattering problems.