The paper addresses the solution of the non-linear EIT inverse reconstruction problem with a priori sparsity information. The authors formulate the EIT reconstruction problem within a hierarchical Bayesian framework, assuming the unknown conductivity to be piecewise constant.
The core of the approach is a hybrid version of the Sparsity Promoting Iterative Alternating Sequential (SP-IAS) algorithm, originally proposed for linear inverse problems and extended here to the non-linear EIT setting. The hybrid SP-IAS algorithm combines the sparsity enhancement of non-convex optimization with the robust convergence properties of the convex setup.
The authors provide a detailed description of the implementation details of the hybrid SP-IAS algorithm, with a specific focus on the parameter selection. The method is then applied to the 2023 Kuopio Tomography Challenge dataset, with a comprehensive report of the running times for different cases and parameter selections.
The numerical results demonstrate the efficiency and flexibility of the proposed algorithm in reconstructing blocky conductivity distributions from limited EIT measurements. The authors highlight that high-quality results can be obtained even when removing up to 8 electrodes from the original setup, with a gain in computing time of about 20%.
إلى لغة أخرى
من محتوى المصدر
arxiv.org
الرؤى الأساسية المستخلصة من
by Monica Pragl... في arxiv.org 05-07-2024
https://arxiv.org/pdf/2405.03343.pdfاستفسارات أعمق