Biblio
Filters: Keyword is inverse problems [Clear All Filters]
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2019. Dealing with Correlation and Sparsity for an Effective Exploitation of the Compressive Processing in Electromagnetic Inverse Problems. 2019 13th European Conference on Antennas and Propagation (EuCAP). :1–4.
In this paper, a novel method for tomographic microwave imaging based on the Compressive Processing (CP) paradigm is proposed. The retrieval of the dielectric profiles of the scatterers is carried out by efficiently solving both the sampling and the sensing problems suitably formulated under the first order Born approximation. Selected numerical results are presented in order to show the improvements provided by the CP with respect to conventional compressive sensing (CSE) approaches.
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2019. Density Imaging Using a Compressive Sampling DBIM approach. 2019 International Conference on Advanced Technologies for Communications (ATC). :160–163.
Density information has been used as a property of sound to restore objects in a quantitative manner in ultrasound tomography based on backscatter theory. In the traditional method, the authors only study the distorted Born iterative method (DBIM) to create density images using Tikhonov regularization. The downside is that the image quality is still low, the resolution is low, the convergence rate is not high. In this paper, we study the DBIM method to create density images using compressive sampling technique. With compressive sampling technique, the probes will be randomly distributed on the measurement system (unlike the traditional method, the probes are evenly distributed on the measurement system). This approach uses the l1 regularization to restore images. The proposed method will give superior results in image recovery quality, spatial resolution. The limitation of this method is that the imaging time is longer than the one in the traditional method, but the less number of iterations is used in this method.
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2019. High-resolution Three-dimensional Microwave Imaging Using a Generative Adversarial Network. 2019 International Applied Computational Electromagnetics Society Symposium - China (ACES). 1:1—3.
To solve the high-resolution three-dimensional (3D) microwave imaging is a challenging topic due to its inherent unmanageable computation. Recently, deep learning techniques that can fully explore the prior of meaningful pattern embodied in data have begun to show its intriguing merits in various areas of inverse problem. Motivated by this observation, we here present a deep-learning-inspired approach to the high-resolution 3D microwave imaging in the context of Generative Adversarial Network (GAN), termed as GANMI in this work. Simulation and experimental results have been provided to demonstrate that the proposed GANMI can remarkably outperform conventional methods in terms of both the image quality and computational time.