WIDE-BAND BUTTERFLY NETWORK: STABLE AND EFFICIENT INVERSION VIA MULTI-FREQUENCY NEURAL NETWORKS

We introduce an end-to-end deep learning architecture called the wide-band butter-fly network (WIDEBNET) for approximating the inverse scattering map from wide-band scattering data. This architecture incorporates tools from computational harmonic analysis, such as the but-terfly factorization, and traditional multi-scale methods, such as the Cooley-Tukey FFT algorithm, to drastically reduce the number of trainable parameters to match the inherent complexity of the problem. As a result, WIDEBNET is efficient: it requires fewer training points than off-the-shelf architectures and has stable training dynamics which are compatible with standard weight initializa-tion strategies. The architecture automatically adapts to the dimensions of the data with only a few hyp er-parameters that the user must specify. WIDEBNET is able to produce images that are com-petitive with optimization-based approaches, but at a fraction of the cost, and we also demonstrate numerically that it learns to super-resolve scatterers with a full aperture configuration.