Sparsity Regularized Nonlinear Inversion for Microwave Imaging

U. Taskin, Ozgur Ozdemir

    Research output: Contribution to journalArticleScientificpeer-review

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number8067638
    Pages (from-to)2220-2224
    Number of pages5
    JournalIEEE Geoscience and Remote Sensing Letters
    Volume14
    Issue number12
    DOIs
    Publication statusPublished - 1 Dec 2017

    Keywords

    • Compressive sensing
    • inverse scattering
    • microwave imaging
    • sparsity regularization
    • wavelet transform

    Fingerprint Dive into the research topics of 'Sparsity Regularized Nonlinear Inversion for Microwave Imaging'. Together they form a unique fingerprint.

    Cite this