Quality-factor inspired deep neural network solver for solving inverse scattering problems
Journal:
arXiv
Published Date:
Apr 29, 2025
Abstract
Deep neural networks have been applied to address electromagnetic inverse
scattering problems (ISPs) and shown superior imaging performances, which can
be affected by the training dataset, the network architecture and the applied
loss function. Here, the quality of data samples is cared and valued by the
defined quality factor. Based on the quality factor, the composition of the
training dataset is optimized. The network architecture is integrated with the
residual connections and channel attention mechanism to improve feature
extraction. A loss function that incorporates data-fitting error,
physical-information constraints and the desired feature of the solution is
designed and analyzed to suppress the background artifacts and improve the
reconstruction accuracy. Various numerical analysis are performed to
demonstrate the superiority of the proposed quality-factor inspired deep neural
network (QuaDNN) solver and the imaging performance is finally verified by
experimental imaging test.
