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Born Iterative Method Based on Convolutional Dictionary Learning for Inverse Scattering Problems
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DOI：10.1109/LAWP.2022.3201618.png)
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Abstract
En 中文
In this letter, a Born iterative method based on convolutional dictionary learning is proposed to solve inverse scattering problems. The method is inspired by compressed sensing, which indicates that the contrast can be represented by nonzero expansion coefficients with respect to expansion bases. The inverse scattering problem is changed from solving the contrast to solving the nonzero expansion coefficients under the expansion bases. In our method, the bases are obtained by convolutional dictionary learning from a set of training data, which learn and characterize the intrinsic laws of the local contrast. The performance of the proposed method is verified using simulated data. We compare it with the Born iterative method and the contrast source inversion method, and the results show that the proposed method can get better results.
Keywords:
Inverse problems
Dictionaries
Convolutional codes
Scattering
Machine learning
Electric fields
Iterative methods
Born iterative method (BIM)
compressed sensing
convolutional dictionary learning (CDL)
inverse scattering
Journal
IF:
4.8
Papers:
1.0W
Citations:
2.8W