Semantic regularization of electromagnetic inverse problems.

Area of Science:

• Applied Physics
• Computer Science
• Artificial Intelligence

Background:

• Solving ill-posed inverse problems traditionally relies on mathematical or quantitative data-driven prior knowledge for regularization.
• Semantically formulated prior knowledge, derived from human reasoning, has been excluded from this process.
• This limitation hinders advanced applications requiring nuanced understanding of the scene.

Purpose of the Study:

• To introduce and demonstrate semantic regularization using pre-trained large language models (LLMs).
• To overcome the limitation of excluding semantically formulated prior knowledge in inverse problem solving.
• To enable new privacy protection capabilities in imaging applications.

Main Methods:

• Numerical simulation of a 2D inverse scattering problem.
• Experimental validation in 3D and 4D compressive microwave imaging using programmable metasurfaces.
• Integration of a pre-trained large language model for semantic regularization.

Main Results:

• Successful application of semantic regularization in both numerical and experimental inverse problems.
• Demonstration of privacy protection through subject concealment and alteration in reconstructions.
• Control over reconstructions achieved via language-based commands manipulating semantic priors.

Conclusions:

• Semantic regularization offers a novel approach to incorporating human-like reasoning into inverse problem solving.
• This technique unlocks advanced privacy-preserving functionalities for applications like smart homes and security screening.
• The use of LLMs paves the way for more intuitive and flexible control in imaging and reconstruction processes.