Abstract
Electrical tomographic imaging (ETI) is an advanced technique that has been widely applied in many fields. But it’s application is greatly limited by its low spatial resolution which mainly caused by two inherent problems: ill-posed solution and soft field effect. Even many optimizing methods have been applied to overcome these problems and have made some contributions to increase the ETI spatial resolution. Meanwhile, the ‘prior information’ method has been demonstrated to be efficient and feasible. In this paper, we introduced the ‘prior information’ method for the optimization of the spatial resolution of ETI process. To do this, we first recovered rich types of the prior information in an ETI process, and then introduced them to the ETI process. Based on the Tikhonov regularization and a traditional optimization method, this assessable prior information can greatly increase spatial resolution for typical flow patterns. Moreover, the reconstructed ETI optimal solution is more robust than the solutions achieved by other existing methods. Both theoretical analysis and experimental results strongly confirmed the priority of this new method. It also laid an important foundation for further wide usage of the prior information to improve the spatial resolution in other applications.
Published Version
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