A reconstruction algorithm for electrical capacitance tomography via total variation and l0-norm regularizations using experimental data

TL;DR

This paper introduces a novel reconstruction algorithm for electrical capacitance tomography that combines total variation and l0-norm regularizations, demonstrating improved image quality through experimental validation.

Contribution

It presents the first application of an l0-norm regularization algorithm (DEPIHT) for ECT, integrated with AADMM for enhanced image reconstruction.

Findings

The AADMM-DEPIHT algorithm outperforms traditional methods in image quality.

Experimental results validate the feasibility and effectiveness of the proposed method.

The DEPIHT algorithm is suitable for post-processing in ECT applications.

Abstract

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.