| Citation: | ZHANG Lifeng. Compressed sensing application to electrical capacitance tomography[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(11): 2316-2321. doi: 10.13700/j.bh.1001-5965.2017.0052(in Chinese)
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Based on the sparsity or compressibility of the signal, compressed sensing (CS) theory can achieve high-accuracy reconstruction of the signal by sampling a small amount of data. In this paper, CS theory was used for the image reconstruction of electrical capacitance tomography (ECT). First, using the fast Fourier transformation (FFT) basis, the gray signals of original images can be transformed into the sparse signals. Then, the random observation matrix of ECT system was designed by rearranging the rows of the sensitivity matrix of ECT in a random order. Finally, interior point method, gradient projection for sparse reconstruction (GPSR) algorithm and greedy algorithm which are the three commonly used reconstruction algorithms of CS were used for ECT image reconstruction and the comparison was made with linear back projection algorithm and Landweber iterative algorithm. Simulation results indicate that reconstructed images with higher accuracy can be obtained using the ECT image reconstruction algorithm based on CS theory. Meanwhile, the advantages and disadvantages of the three CS image reconstruction algorithms were analyzed. The advice of selecting which type of image reconstruction algorithm was given.
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