Horizontal Distortion Correction of AFM Images Based on Automatic Labeling of Feature Graphics

The atomic force microscope (AFM) image will be inclined and bent due to the tilt angle between the probe and the sample surface. When the least squares fitting method is used to correct the horizontal distortion of the AFM image, the shape structure that is lower or higher than the sample base will affect the final fitting correction result. In view of the limitations of existing methods and the diversity of AFM images, an AFM image level distortion correction method based on automatic feature marking is proposed. The Canny edge detection algorithm with adaptive threshold is used to automatically detect and recognize the feature graphics, and the feature graphics are zeroed by the hole filling algorithm to automatically remove the feature graphics data from the line fitting data. After completing the least squares fitting correction of all the rows of data, the full AFM image data with eliminating horizontal distortion can be obtained. Finally, the corrected full image data are used for imaging. This method can be adapted to different types of AFM images and realize automatic fitting correction of the whole image, which improves the accuracy and efficiency of correction.