Reconstruction of atomic force microscopy image using compressed sensing

Abstract

Atomic Force Microscopy (AFM) is one of the most popular and advanced tools for ultra high-resolution imaging and nanomanipulation of nano-scale matter. But AFM imaging typically takes a long time. High-speed and high-precision AFM measurement has attracted wide attention in recent several years. In traditional AFM, simple reduction in the number of measurement points may lose essential sample topography information. To resolve such problems, an AFM image reconstruction method based on Compressed Sensing (CS) theory is applied to reduce image acquisition time without cutting down the image quality. The benefit of using CS approach in AFM is shortening the imaging time, minimizing the interaction with the sample, and finally avoiding sample damage in AFM. Three kinds of testing samples with high and low frequency components were examined by a scanning electron microscope (SEM) and by AFM. An orthogonal Matching Pursuit (OMP) algorithm is employed to reconstruct an AFM image with different sampling rates. Subsequently the reconstruction results of sample topography images are analyzed and evaluated. Using the CS approach in AFM can greatly improve the AFM imaging process. Experimental results show that the obtained reconstructed images have different resolution and quality, depending on the surface morphology of the sample and sampling rates.