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| Study on the sparse imaging method of deep learning electrical impedance block based on DK-SVD |
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Abstract iming at the ill-posedness and nonlinearity of electrical impedance tomography inverse problem, a DK-SVD-based block sparse image reconstruction method is proposed. The multi-layer perceptron is introduced to finetune optimal model parameters for measurement data considering the complexity of datasets and improve the image quality. The iterative shrinkage threshold algorithm is used to accelerate convergence in the sparse coding stage. The simulation results show that the structural similarity of the reconstructed image by DK-SVD algorithm can reach more than 0.95, the error can be controlled at about 0.1, and the average reconstruction speed is 0.034s, which effectively improves the quality and efficiency of electrical impedance tomography, and further experiments prove that the algorithm has good noise robustness and practical application value.
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Received: 18 August 2023
Published: 26 September 2024
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2024, Vol. 45 
