Research on image reconstruction algorithms based on autoencoder neural network of Restricted Boltzmann Machine (RBM)

Aiming at the problem of low quality in image reconstruction of traditional image reconstruction algorithm of electromagnetic tomography(EMT), an EMT image reconstruction algorithm based on autoencoder neural network of Restricted Boltzmann Machine (RBM) is proposed. Firstly, the basic principles of EMT system and autoencoder neural network are analyzed. Autoencoder neural network is a deep learning model, which contains two parts: encoder and decoder. The encoding process of the encoder is equivalent to the object field detection process in the EMT system; the decoding process of the decoder is equivalent to the image reconstruction process. On this basis, an autoencoder neural network model is built. In this model, the RBM is used for layer by layer pretraining to obtain the initial weight and offset, and the global weight and offset are adjusted by BP algorithm. The parameter file generated in the trained autoencoder neural network is used to construct a decoder. Finally, the detected voltage value output by the EMT system is input into the decoder network to obtain the reconstructed image of the EMT. Furthermore, data with Gaussian noise and data regarding flow pattern not in training dataset are used to test the generalization ability and practicability of the network, respectively. The experimental results show that the method in this paper is a kind of EMT image reconstruction method with higher accuracy, which also provides a new means for EMT image reconstruction.

Automated summary

The paper introduces an electromagnetic tomography (EMT) image reconstruction algorithm based on a Restricted Boltzmann Machine (RBM) autoencoder neural network, achieving higher accuracy in image reconstruction through deep learning.