IFNet: Imaging and Focusing Network for handheld mmWave Devices

Recent advancements have showcased the potential of hand-held millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve imaging and focusing for handheld mmWave systems. By integrating multiple priors and mapping the optimization processes into an iterative network structure, IFNet effectively compensates for phase errors and recovers high-fidelity images from severely distorted signals. Extensive experiments demonstrate that IFNet outperforms state-of-the-art methods, both qualitatively and quantitatively.