3-D Super-Resolution of Coded Aperture Millimeter-Wave Images Using Complex-Valued Convolutional Neural Network

Abstract

Electromagnetic (EM) waves at millimeter-wave (mmW) frequencies have found applications in a variety of imaging systems, from security screening to defense and automotive radars, with the research and development of mmW imaging systems gaining interest in recent years. Despite their significant advantages, mmW imaging systems suffer from poor resolution compared to higher frequency reconstructions, such as optical images. To improve the resolution of mmW images, various super-resolution (SR) techniques have been introduced. One such technique is the use of machine learning algorithms in the signal processing layer of the imaging system without altering any of the system’s parameters. This article focuses on the use of a convolutional neural network (CNN) architecture to achieve SR when applied to 3-D mmW input images. To exploit the phase information content of the input images along with the magnitude, a complex-valued CNN is designed, which can accommodate complex-valued data. To simplify the learning process, the resolution difference between the input and output images is divided into smaller parts by using subnetworks in the CNN architecture. The trained model is tested on simulated and experimental targets. The average mean square error score and the structural similarity index obtained on a test dataset of 460 samples are 0.0127 and 0.9225, respectively. It can be inferred that the model has the capability to improve the resolution of input mmW images to a high degree of fidelity, hence paving the way for an end-to-end SR imaging system.