  Electrical capacitance tomography (ECT) has been investigated in many fields
due to its advantages of being non-invasive and low cost. Sparse algorithms
with l1-norm regularization are used to reduce the smoothing effect and obtain
sharp images, such as total variation (TV)regularization. This paper proposed
for the first time to solve the ECT inverse problem using an l0-norm
regularization algorithm, namely the doubly extrapolated proximal iterative
hard thresholding (DEPIHT) algorithm. The accelerated alternating direction
method of multipliers (AADMM) algorithm, based on the TV regularization, has
been selected to acquire the first point for the DEPIHT algorithm. Experimental
tests were carried out to validate the feasibility of the AADMM-DEPIHT
algorithm,which is compared with the Landweber iteration (LI) and AADMM
algorithms. The results show the AADMM-DEPIHT algorithm has an improvement on
the quality of images and also indicates that the DEPIHT algorithm can be a
suitable candidate for ECT in post-process.

Signal Processing