Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images - Samuel Gelman; Irit Rosenhek-Goldian; Nir Kampf; Marek Patočka; Maricarmen Rios; Marcos Penedo; Georg Fantner; Amir Beker; Sidney R. Cohen; Ido Azuri (Beilstein Journal of Nanotechnology, 2025)
In this study, we employed traditional methods and deep learning models to improve resolution and quality of low-resolution AFM images made under standard ambient scanning. Both traditional methods and deep learning models were benchmarked and quantified regarding fidelity, quality, and a survey taken by AFM experts. The deep learning models outperform the traditional methods and yield better results. Additionally, some common AFM artifacts, such as streaking, are present in the ground truth high-resolution images. These artifacts are partially attenuated by the traditional methods but are completely eliminated by the deep learning models. This work shows deep learning models to be superior for super-resolution tasks and enables significant reduction in AFM measurement time, whereby low-pixel-resolution AFM images are enhanced in both resolution and fidelity through deep learning.