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In this paper, a high noise immune time-domain inversion cascade network(TICaN) is proposed to reconstruct scatterers from the measured electromagneticfields. The TICaN is comprised of a denoising block aiming at improving thesignal-to-noise ratio, and an inversion block to reconstruct theelectromagnetic properties from the raw time-domain measurements. Thescatterers investigated in this study include complicated geometry shapes andhigh contrast, which cover the stratum layer, lossy medium and hyperfinestructure, etc. After being well trained, the performance of the TICaN isevaluated from the perspective of accuracy, noise-immunity, computationalacceleration, and generalizability. It can be proven that the proposedframework can realize high-precision inversion under high-intensity noiseenvironments. Compared with traditional reconstruction methods, TICaN avoidsthe tedious iterative calculation by utilizing the parallel computing abilityof GPU and thus significantly reduce the computing time. Besides, the proposedTICaN has certain generalization ability in reconstructing the unknownscatterers such as the famous Austria rings. Herein, it is confident that theproposed TICaN will serve as a new path for real-time quantitative microwaveimaging for various practical scenarios.

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