Recently, metasurfaces have experienced revolutionary growth in the sensing
and superresolution imaging field, due to their enabling of subwavelength
manipulation of electromagnetic waves. However, the addition of metasurfaces
multiplies the complexity of retrieving target information from the detected
fields. Besides, although the deep learning method affords a compelling
platform for a series of electromagnetic problems, many studies mainly
concentrate on resolving one single function and limit the research's
versatility. In this study, a multifunctional deep neural network is
demonstrated to reconstruct target information in a metasurface targets
interactive system. Firstly, the interactive scenario is confirmed to tolerate
the system noises in a primary verification experiment. Then, fed with the
electric field distributions, the multitask deep neural network can not only
sense the quantity and permittivity of targets but also generate
superresolution images with high precision. The deep learning method provides
another way to recover targets' diverse information in metasurface based target
detection, accelerating the progression of target reconstruction areas. This
methodology may also hold promise for inverse reconstruction or forward
prediction problems in other electromagnetic scenarios.

本研究表明，多功能深度神经网络可用于在互动式元表面系统中重建目标信息，并且能够通过依赖电场分布，感知目标的数量和介电常数，并生成具有高精度的高超分辨图像。