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Quality-Factor-Inspired Deep Neural Network Solver for Solving Inverse Scattering Problems
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DOI：10.1109/TGRS.2025.3609332.png)
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Abstract
En 中文
Deep neural networks (DNNs) 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 concerned 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 analyses are performed to demonstrate the superiority of the proposed quality-factor-inspired DNN (QuaDNN) solver, and the imaging performance is finally verified by experimental imaging tests.
Keywords:
Data composition
deep learning
inverse scattering problems (ISPs)
loss function
physical constraints
quality factor
Journal
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2.1W
Citations:
10.7W