The use of an LSTM-based autoencoder for measurement denoising in process tomography

Abstract

The main problem with any tomography is the transformation of measurements into images. It is the so-called “inverse problem”, which, due to its indeterminacy, can never be solved perfectly. An additional factor contributing to the deterioration of the quality of tomograms is measurement noise. This article shows how to denoise electrical capacitance tomography measurements using the LSTM autoencoder. The presented model is two-staged. First, the autoencoder is trained using very noisy measurements. Then, the decoder autoencoder generates a training set to using activations ofe the latent layer. In the second stage, the LSTM network is trained, which has encoder latent layer activations at the input and pattern images at the output. The results of the experiments show that using an autoencoder to denoise the measurements improves the reconstruction quality.