Microwave Imaging of Nonsparse Domains Using Born Iterative Method With Wavelet Transform and Block Sparse Bayesian Learning


A microwave technique utilizing the mix of wavelet transform, block sparse Bayesian learning (BSBL), and Born iterative method (BIM) is proposed to image nonsparse domains. The wavelet transform is implemented to convert the nonsparse domain into a sparse domain. Then, BSBL framework based on expectation-maximization (EM) algorithm is applied on the BIM model to reconstruct the first profile of the nonsparse domain. The presented imaging results of a nonsparse model indicate that the proposed technique, compared with traditional microwave imaging or compressive-sensing (CS) algorithms, achieve very low normalized error rate (NER) at a short computational time using only little number of antennas. The accuracy, robustness, and effectiveness of the proposed methodology are additional assessed by using it to detect a hemorrhagic brain stroke in a realistic, numerical head model, which may be a nonsparse domain. The obtained results indicate the aptitude for the technique to detect an early stroke in the realistic nonsparse atmosphere of the human head using solely six antennas.

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