Gaussian reconstruction algorithm for under-sampling atomic force microscopy images based on cassini elliptical scanning

Non-grid scanning of atomic force microscope can improve the imaging speed compared with traditional grid scanning, but when the speed is increased, the imaging quality may decrease. In this paper, the AFM system is built by ourselves, and the standard grid and human vascular smooth muscle cells are imaged by Cassini ellipse under-sampling. The samples of Cassini under-sampling are reconstructed by Gaussian regression, and compared with the traditional cubic spline interpolation reconstruction method. The results show that Gaussian regression has obvious improvement over traditional interpolation reconstruction for standard samples. When scanning cells, keep the under-sampling rate greater than 45%, so as to ensure a better reconstruction result.