Abstract Due to the limitations of physical models, the traditional algorithm of electromagnetic tomography (EMT) leads to the lack of reconstruction data, which makes its inverse problem have serious discomfort and pathology. In order to solve the problems of many artifacts and poor quality in the reconstructed images, a composite electromagnetic tomography algorithm based on MLP-AE is proposed. Firstly, the field information of the object to be tested is sent to the self-coding neural network (AE) for learning as input to obtain the encoded data. Then the electromagnetic excitation of the measured object field is carried out to obtain voltage data. The voltage data is taken as input, and the data after encoding the field information of the DUT is sent to the MLP neural network for learning as output. Finally decoding enables end-to-end image reconstruction. The performance of the proposed algorithm is evaluated by mean squared error, structural similarity index and imaging time, and compared with the linear backprojection algorithm, Tikhonov regularization algorithm, and Landweber iterative algorithm. The experimental results show that the proposed algorithm reduces the mean squared error by 28.77%, 22.57% and 23.74% compared with the above traditional algorithms on a single image, the structural similarity index is increased by 17.54%, 14.38% and 15.44%, and the imaging time is 73.78%, 98.63% and 93.86% faster, respectively. It provides an idea for real-time accurate imaging later.
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2024, Vol. 45 
