Random Sampling With Iterative Recovery for Millimeter-Wave Imaging Systems

In this brief, an imaging methodology for mono-static millimeter-wave systems is introduced. When the measured object hologram is piece-wise smooth with sharp boundaries, both low-pass and high-pass components are present in a transform domain. Nevertheless, the edges are sparse in transform domain with respect to the image dimensions. It is shown that the down-sampling of the hologram with an appropriate rate determined by the sparsity level, includes the adequate information of all components for image reconstruction. Using random samples, an algorithm is proposed to recover all the sparse components, iteratively. Simulation results illustrate that a high-resolution recovery can be achieved. For instance, by using 20% of the randomly selected hologram samples, an image recovery with a PSNR of 20 dB is achieved and the object scan time is significantly reduced. The proposed methodology is applied to a practical imaging system operating at 30 GHz, where the results illustrate a successful reconstruction of the images.