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We propose an end-to-end deep learning framework that comprehensively solvesthe inverse wave scattering problem across all length scales. Our frameworkconsists of the newly introduced wide-band butterfly network coupled with asimple training procedure that dynamically injects noise during training. Whileour trained network provides competitive results in classical imaging regimes,most notably it also succeeds in the super-resolution regime where othercomparable methods fail. This encompasses both (i) reconstruction of scattererswith sub-wavelength geometric features, and (ii) accurate imaging when two ormore scatterers are separated by less than the classical diffraction limit. Wedemonstrate these properties are retained even in the presence of strong noiseand extend to scatterers not previously seen in the training set. In addition,our network is straightforward to train requiring no restarts and has an onlineruntime that is an order of magnitude faster than optimization-basedalgorithms. We perform experiments with a variety of wave scattering mediumsand we demonstrate that our proposed framework outperforms both classicalinversion and competing network architectures that specialize in oscillatorywave scattering data.

Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime