Image Reconstruction Algorithm Based On PCA and WNN for ECT

摘要

Electrical capacitance tomography (ECT) technique is a new technique for two-phase flow measurement. ECT is a complex nonlinear problem. To solve the ill-posed image reconstruction problem, image reconstruction algorithm based on wavelet neural networks (WNN) was presented. The principal component analysis (PCA) method was used to reduce the dimension of the input vectors. The transfer functions of the neurons in the WNN were wavelet base functions which were determined by retract and translation factors. The input measurement data were obtained using the ECT simulation software developed by the author. BP algorithm was used to train the WNN, and self-adaptive learning rate and momentum coefficient were also used to accelerate the learning speed. Experimental results showed the image quality has been improved markedly, compared with the typical linear back projection (IMP) algorithm and Landweber iteration algorithm.