Gelman Samuel, Rosenhek-Goldian Irit, Kampf Nir, Patočka Marek, Rios Maricarmen, Penedo Marcos, Fantner Georg, Beker Amir, Cohen Sidney R, Azuri Ido
Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Beilstein J Nanotechnol. 2025 Jul 16;16:1129-1140. doi: 10.3762/bjnano.16.83. eCollection 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.
在本研究中，我们采用传统方法和深度学习模型来提高在标准环境扫描下获取的低分辨率原子力显微镜（AFM）图像的分辨率和质量。针对保真度、质量以及AFM专家进行的一项调查，对传统方法和深度学习模型都进行了基准测试和量化。深度学习模型优于传统方法并产生了更好的结果。此外，一些常见的AFM伪像，如条纹，存在于真实的高分辨率图像中。这些伪像通过传统方法得到了部分减弱，但被深度学习模型完全消除。这项工作表明深度学习模型在超分辨率任务中更具优势，并能显著减少AFM测量时间，通过深度学习可提高低像素分辨率AFM图像的分辨率和保真度。
