Image inpainting is a critical computer vision task to restore missing or
damaged image regions. In this paper, we propose Trans-Inpainter, a novel
multimodal image inpainting method guided by Channel State Information (CSI)
data. Leveraging the power of transformer architectures, Trans-Inpainter
effectively extracts visual information from CSI time sequences, enabling
high-quality and realistic image inpainting. To evaluate its performance, we
compare Trans-Inpainter with RF-Inpainter, the state-of-the-art radio frequency
(RF) signal-based image inpainting technique. Through comprehensive
experiments, Trans-Inpainter consistently demonstrates superior performance in
various scenarios. Additionally, we investigate the impact of CSI data
variations on Trans-Inpainter's imaging ability, analyzing individual sensor
data, fused data from multiple sensors, and altered CSI matrix dimensions.
These insights provide valuable references for future wireless sensing and
computer vision studies