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Sparsity Regularized Nonlinear Inversion for Microwave Imaging
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DOI：10.1109/LGRS.2017.2757087.png)
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Abstract
En 中文
We present a novel microwave imaging technique for sparse domain imaging applications. In the proposed method, inverse scattering algorithm modified gradient method (MGM) is combined with a fast iterative shrinkage-thresholding algorithm to improve the resolution and robustness of the MGM by enforcing the sparsity in the imaging domain. The numerical experiments show that the proposed method achieves higher resolution and robustness compared with that of classical MGM. For nonsparse domain reconstruction, the wavelet transformation is adopted to convert nonsparse spatial domain into a sparse wavelet coefficient domain. The feasibility of the proposed method in the wavelet domain is demonstrated through the numerical experiments.
Keywords:
Compressive sensing
inverse scattering
microwave imaging
sparsity regularization
wavelet transform
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