Reconstruction of high-resolution atomic force microscopy measurements from fast-scan data using a Noise2Noise algorithm
Eva Natinsky, Ryan M. Khan, Michael Cullinan, Rémi Dingreville
Abstract
The acquisition of large atomic-force-microscopy (AFM) scans at nanoscale resolutions can take hours and produce datasets with millions of pixels, which is time consuming and computationally expensive to analyze. In this paper, we present an approach to speed up this process by using a computer-vision algorithm, namely the Noise2Noise algorithm, to reconstruct high-resolution, low scan speed AFM data from high-speed, noisy, sparsely sampled AFM data. This algorithm is trained on various noise types to reproduce different sources of experimental noises encountered during the acquisition of AFM data. Our results demonstrate that a sparse, uniform AFM scan of 20 × 20 μm at 128 × 128 pixel resolution can be processed within seconds, and the output image is comparable to a higher quality raw data scan which required 30 min or more to collect, reducing not only the acquisition and analysis time, but also the size of the data being collected.