Wykaz obszarów badawczych związanych z tagiem Inverse-problem:
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| 1 |
“Deep neural networks for image reconstruction in electrical capacitance tomography” Electrical capacitance tomography (ECT) enables the visualization of the spatial distribution of an object’s electrical permittivity. Image reconstruction in ECT presents a significant challenge as it is an ill-posed and ill-conditioned inverse problem. Advanced nonlinear algorithms, such as the Levenberg-Marquardt method, are iterative and computationally intensive, primarily due to the repeated calculation of the Jacobian matrix. Deep neural networks (DNNs) have emerged as a promising alternative for image reconstruction. This work will explore deep network architectures that not only match but significantly outperform classical methods in reconstruction quality. Synthetic data will be employed for supervised learning, and the performance of DNNs will be tested against real measurements. Application-specific training datasets will be analyzed, and their size will be expanded using data augmentation techniques. A comparative evaluation of various training datasets will also be conducted. To optimize the network, different loss functions and solvers will be utilized. Reconstructed images will be generated using DNNs and compared against a baseline provided by the Levenberg-Marquardt algorithm. Results will be assessed using selected image quality metrics. It is anticipated that deep networks will enhance the spatial resolution of ECT scanners. The insights gained from selecting deep networks, analyzing their architectures, and refining training strategies can potentially be applied to solve inverse problems in other fields.
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