The atomic force microscope (AFM) has become a powerful tool in many fields. However, environmental noise and other disturbances are very likely to cause the AFM probe to vibrate, which lead to vertical drift in AFM imaging and limit its further application. Therefore, to correct image distortion caused by vertical drift, a morphology prediction based image correction algorithm is proposed in this paper. Specifically, a Gaussian–Hann filter is first designed for distorted AFM images, based on which, an adaptive image binarization algorithm is developed to achieve accurate object detection and background extraction. Furthermore, an advanced morphology prediction algorithm, consisting of morphological approximation prediction and morphological detail prediction, is proposed to correct image distortion by using the extracted substrate of a sample image. Approximate morphology is generated by an improved weighted fusion autoregressive model, and morphological detail is obtained by energy analysis based on discrete wavelet transform. Experimental and application results are presented to illustrate that the proposed algorithm is able to effectively eliminate vertical drift of AFM images.

原子力显微镜 (AFM) 已成为许多领域的有力工具。然而，环境噪声和其他干扰很可能导致 AFM 探头振动，从而导致 AFM 成像的垂直漂移并限制其进一步应用。因此，为了校正由垂直漂移引起的图像失真，本文提出了一种基于形态学预测的图像校正算法。具体而言，首先针对失真的 AFM 图像设计了高斯-汉恩滤波器，在此基础上开发了自适应图像二值化算法以实现准确的目标检测和背景提取。此外，一种先进的形态预测算法，包括形态近似预测和形态细节预测，建议通过使用样本图像的提取基板来校正图像失真。近似形态由改进的加权融合自回归模型生成，形态细节通过基于离散小波变换的能量分析获得。实验和应用结果表明，该算法能够有效消除AFM图像的垂直漂移。