LAUSR

Three-dimensional multistatic imaging is a powerful noninvasive examination tool for many military and civilian applications. Recently, the sparsity-regularized optimization has been used as a popular imaging technique to enhance the image quality. However, it suffers from the expensive computational cost, since its solution is obtained by a time-consuming iterative scheme, which is typically computationally prohibitive for large-scale imaging problems. To overcome this difficulty, this challenging imaging problem is converted into an image processing problem in this letter, which can be performed over small-scale overlapping patches and be efficiently solved in a parallel or distributed manner. In this way, the proposed qualitative scheme could be utilized to solve large-scale imaging problems. Exemplary simulation results are provided to demonstrate the efficiency of the proposed methodology.