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Damp walls of buildings are a serious problem due to the social and economic consequences. Moisture causes accelerated wear of facades, paint coatings, weakening of the wall structure, and high maintenance and renovation costs. The growth of fungi and bacteria worsens the indoor microclimate [1]. Effective identification of moisture inside the walls enables effective preventive actions. The paper presents an algorithmic concept that increases the quality of tomographic images showing the distribution of moisture inside the walls. The method solves the problem of monitoring the dampness of historical buildings and walls susceptible to moisture. The research focuses on solving the inverse problem of converting electrical measurements into spatial images. The study used a proprietary electrical impedance tomography system with specially designed electrodes. The measurement vector is converted to images in two stages. In the first stage, the Long Short-Term Memory (LSTM) neural network was used, which generates raw reconstructions. The task of the second LSTM network is to convert the raw images obtained in the first stage into enhanced images. The application of the presented method is not limited to one type of narrow-sphere tomography. The two-stage approach can be easily adapted to, e.g., medical and industrial or process tomography. Therefore, it is a generic, universal method with great implementation potential, which is its great advantage.
