Near-field millimeter-wave and visible image fusion via transfer learning.

Abstract

To facilitate penetrating-imaging oriented applications such as nondestructive internal defect detection and localization under obstructed environment, a novel pixel-level information fusion strategy for mmWave and visible images is proposed. More concretely, inspired by both the advancement of deep learning on universal image fusion and the maturity of near-field millimeter wave imaging technology, an effective deep transfer learning strategy is presented to capture the information hidden in visible and millimeter wave images. Furthermore, by implementing fine-tuning strategy and by using an improved bilateral filter, the proposed fusion strategy can robustly exploit the information in both the near-field millimeter wave field and the visual light field. Extensive experiments imply that the proposed strategy can provide superior performance in terms of accuracy and robustness under real-world environment.