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Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery.

, , , and . ICASSP, page 7804-7808. IEEE, (2014)

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Radar Stationary and Moving Indoor Target Localization with Low-rank and Sparse Regularizations., , and . ICASSP, page 2172-2176. IEEE, (2019)Depth Image Reconstruction Using Low Rank and Total Variation Representations., and . INISCOM, volume 334 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 181-194. Springer, (2020)A Hyperspectral Image Denoising Approach via Low-Rank Matrix Recovery and Greedy Bilateral., , and . RIVF, page 1-6. IEEE, (2021)A Non-local Low Rank and Total Variation Approach for Depth Image Estimation., , , , and . RIVF, page 1-6. IEEE, (2021)Compressive Radar Imaging of Stationary Indoor Targets With Low-Rank Plus Jointly Sparse and Total Variation Regularizations., , and . IEEE Trans. Image Process., (2020)Enhanced wall clutter mitigation for compressed through-the-wall radar imaging using joint Bayesian sparse signal recovery., , , and . ICASSP, page 7804-7808. IEEE, (2014)Multipolarization Through-Wall Radar Imaging Using Low-Rank and Jointly-Sparse Representations., , and . IEEE Trans. Image Process., 27 (4): 1763-1776 (2018)Multi-view indoor scene reconstruction from compressed through-wall radar measurements using a joint bayesian sparse representation., , , and . ICASSP, page 2419-2423. IEEE, (2015)Multi-polarization through-the-wall radar imaging using joint Bayesian compressed sensing., , and . DSP, page 783-788. IEEE, (2014)Pooling-Based Feature Extraction and Coarse-to-fine Patch Matching for Optical Flow Estimation., , , and . ACCV (4), volume 11364 of Lecture Notes in Computer Science, page 597-612. Springer, (2018)