In order to improve the sampling rate and the quality of the reconstructed images of electrical capacitance tomography (ECT) system

a new ECT image reconstruction algorithm based on compressed sensing theory was proposed.Firstly

using the orthogonal basis of Discrete Fourier Transformation

the gray signals of original images can be transformed into sparse signals.Then

14 electrodes randomly selected from the 16 electrodes ECT system were excited randomly and the capacitance values between different electrode pairs were also measured in a random order.By this way

the capacitance signals and the corresponding observation matrix were obtained.Finally

using L1 regularization model and primal dual interior point method

the gray signals of original images were achieved.The simulation results showed that the quality of the reconstructed images were better than the corresponding images obtained by the Landweber iterative algorithm.Therefore

the algorithm proposed can reconstruct high precision images with less observation data

which provides a new method for ECT image reconstruction.