Generative Image Restoration and Super-Resolution using Physics-Informed Synthetic Data for Scanning Tunneling Microscopy
PositiveArtificial Intelligence
A new machine learning approach has been proposed to enhance scanning tunneling microscopy (STM) by improving image restoration and super-resolution. This innovation addresses common challenges like tip degradation and slow data acquisition, making STM more effective for atomic-resolution imaging. By utilizing physics-informed synthetic data, this method not only repairs images but also boosts their quality, which is crucial for advancing research in nanotechnology and materials science.
— Curated by the World Pulse Now AI Editorial System


