宽带蝶形网络：基于多频神经网络的稳定高效反演
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
摘要：
我们引入了一种称为宽带蝴蝶网络（WideBNet）的端到端深度学习架构，用于从宽带散射数据近似逆散射图。这种架构结合了计算谐波分析的工具，如蝴蝶因子分解，以及传统的多尺度方法，如Cooley-Tukey FFT算法，以大幅减少可训练参数的数量，以匹配问题的固有复杂性。因此，WideBNet是高效的：它需要比现成架构更少的训练点，并且具有稳定的训练动态，因此它可以依赖于标准的权重初始化策略。该架构自动适应数据的维度，用户只需指定几个超参数。WideBNet能够产生与基于优化的方法竞争的图像，但成本只有一小部分，并且我们还通过数值证明了它可以在全孔径散射设置中学习超分辨散射体。
英文摘要：
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data. This architecture incorporates tools from computational harmonic analysis, such as the butterfly 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, thus it can rely on standard weight initialization strategies. The architecture automatically adapts to the dimensions of the data with only a few hyper-parameters that the user must specify. WideBNet is able to produce images that are competitive with optimization-based approaches, but at a fraction of the cost, and we also demonstrate numerically that it learns to super-resolve scatterers in the full aperture scattering setup.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
