GAN-Based Focusing-Enhancement Method for Monochromatic Synthetic Aperture Imaging

Two-dimensional (2-D) synthetic aperture imaging with a single frequency suffers from limited depth-of-focus (DOF), and leads to the difficulty of focusing volume targets. In this paper,as opposed to using a wide band for 3-D imaging, this out-of-focus problem is examined as a multi-focal imaging issue. To solve the limited DOF problem, we propose a generative adversarial network (GAN) based focusing-enhancement method (GAN-FEM) to fit an unknown out-of-focus kernel for MMW monochromatic synthetic aperture imaging. To determine which type of MMW-images dataset of input can be better suitable for GAN, the grayscale and pseudo-color images dataset are tested respectively to train the neural network. Proof-of-principle experiments are performed at 94 GHz and the results prove that our proposed GAN-FEM can greatly improve the focusing performance for volume targets. The effectiveness of our proposed method confirms the focusing-enhancement capacity of 2-D monochromatic imaging system for 3-D targets, and provides a possible solution to reduce the system complexity for practical 3-D imaging missions.