Measurement Geometry and Design for Trustworthy Generative Inverse Problems

Researchers have proposed new frameworks for addressing generative inverse problems and metasurface synthesis, improving trustworthy image reconstruction and controllable electromagnetic wave manipulation.

A recent study on generative inverse problems approaches the issue from a measurement-geometry perspective to enhance trustworthy image reconstruction, particularly in medical imaging where scan-time, dose, and calibration constraints are significant^[1]. Generative models used as priors for inverse problems can create trust issues due to plausible but potentially inaccurate reconstructions. To address this, the researchers introduced a local measurement-manifold compatibility measure that quantifies the relationship between measurements and generative priors, controlling the stable part of the reconstruction error. Meanwhile, a separate study proposed a new generative inverse design framework for metasurface synthesis under continuous spectral constraints, achieving an average mean squared error of 0.0052 and a valid EM design generation percentage of 89.57^[2]. This framework employs a progressively growing Wasserstein generative adversarial network to enable controllable and physically consistent metasurface synthesis. Metasurfaces are crucial for precise manipulation of electromagnetic waves, but their inverse design with targeted EM responses remains challenging.