Sharif Digital Repository

We propose to use a compressed sensing recovery method called IMATCS for improving the resolution in microwave imaging applications. The electromagnetic inverse scattering problem is solved using the Distorted Born Iterative Method combined with the IMATCS algorithm. This method manages to recover small targets in cases where traditional DBIM approaches fail. Furthermore, by applying an L2-based approach to regularize the sparse recovery algorithm, we improve the algorithm's robustness and demonstrate its ability to image complex breast structures. Although our simulation scenarios do not fully represent experimental or clinical data, our results suggest that the proposed algorithm may be... 

This letter presents a fast microwave imaging technique based on the concept of smoothed minimization and adaptive thresholding. The distorted Born iterative method (DBIM) is used to solve the electromagnetic (EM) inverse scattering problem. We propose to solve the set of underdetermined equations at each iteration of the DBIM algorithm using an L2 regularized iterative smoothed adaptive thresholding (L2-ISATCS) technique. Our simulation results confirm that this technique can reduce considerably the required reconstruction times for the DBIM method relative to previously suggested compressed sensing (CS)-based approaches  

The inverse problem in MicroWave Imaging (MWI) is an ill-posed one which can be solved with the aid of the sparsity prior of the solution. In this paper, an Iterative Least Squares Algorithm (ILSA) has been proposed as an inverse solver in MWI which seeks for the sparse vector satisfying the problem constraints. Minimizing a least squares cost function, we derive a relatively simple iterative algorithm which enforces the sparsity gradually with the aid of a reweighting operator. The simulation results confirm the superiority of the suggested method compared to the state-of-the-art schemes in the quality of the recovered breast tumors in the microwave images  

We propose a new inverse scattering technique based on sparsity for the application of microwave imaging. The underdetermined inverse problem appeared in the distorted born iterative method (DBIM) technique is solved using the suggested L2-regularized iterative weighted algorithm (L2-IWA). The L2-regularizer has been introduced to stabilize the algorithm against nonlinear approximations, and the sparsity is enforced with the aid of another reweighted L2-norm regularizer to address the ill-posedness of the inverse problem. The derived algorithm is a three-step iterative technique which solves the underdetermined set of equations at each DBIM iteration. Moreover, the convergence of the L2-IWA... 

We propose a new image recovery method to improve the resolution in microwave imaging applications. Scattered field data obtained from a simplified breast model with closely located targets is used to formulate an electromagnetic inverse scattering problem, which is then solved using the Distorted Born Iterative Method (DBIM). At each iteration of the DBIM method, an underdetermined set of linear equations is solved using our proposed sparse recovery algorithm, IMATCS. Our results demonstrate the ability of the proposed method to recover small targets in cases where traditional DBIM approaches fail. Furthermore, in order to regularize the sparse recovery algorithm, we propose a novel...