Energy reduction with super-resolution convolutional neural network for ultrasound tomography

Daruisz Wójcik, Tomasz Rymarczyk, Bartosz Przysucha, Michal Golabek, Dariusz Majerek, Tomasz Warowny, Manuchehr Soleimani

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)
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This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution measurement. The model is trained on simulation data and validated on real measurement data. The results of this technique demonstrate significant improvement compared to state-of-the-art methods. The study also highlights that UST measurements contain more information than previously thought, and this hidden information can be extracted and utilized with the use of machine learning techniques to further improve image quality and object recognition.
Original languageEnglish
Article number1387
Issue number3
Early online date31 Jan 2023
Publication statusPublished - 1 Feb 2023


  • deep learning
  • energy consumption
  • energy optimization
  • Industry 4.0
  • inverse problems
  • machine learning
  • tomography

ASJC Scopus subject areas

  • Control and Optimization
  • Energy (miscellaneous)
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Building and Construction
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment


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