High Noise Immune Time-domain Inversion via Cascade Network (TICaN) for Complex Scatterers

TL;DR

This paper introduces TICaN, a cascade neural network that enhances electromagnetic scatterer reconstruction accuracy and noise immunity in time-domain imaging, enabling real-time, high-precision results for complex and high-contrast structures.

Contribution

The paper presents a novel cascade network combining denoising and inversion blocks, improving noise robustness and computational efficiency in electromagnetic scatterer reconstruction.

Findings

High noise immunity in scatterer reconstruction

Significant reduction in computation time using GPU acceleration

Good generalization to unknown scatterers like Austria rings

Abstract

In this paper, a high noise immune time-domain inversion cascade network (TICaN) is proposed to reconstruct scatterers from the measured electromagnetic fields. The TICaN is comprised of a denoising block aiming at improving the signal-to-noise ratio, and an inversion block to reconstruct the electromagnetic properties from the raw time-domain measurements. The scatterers investigated in this study include complicated geometry shapes and high contrast, which cover the stratum layer, lossy medium and hyperfine structure, etc. After being well trained, the performance of the TICaN is evaluated from the perspective of accuracy, noise-immunity, computational acceleration, and generalizability. It can be proven that the proposed framework can realize high-precision inversion under high-intensity noise environments. Compared with traditional reconstruction methods, TICaN avoids the tedious iterative calculation by utilizing the parallel computing ability of GPU and thus significantly reduce the computing time. Besides, the proposed TICaN has certain generalization ability in reconstructing the unknown scatterers such as the famous Austria rings. Herein, it is confident that the proposed TICaN will serve as a new path for real-time quantitative microwave imaging for various practical scenarios.