受限于探针针尖结构尺寸，用原子力显微镜进行微纳测量时会产生图像边缘失真.提出了一种基于迁移学习的原子力显微镜成像恢复方法，通过迁移学习训练源模型和靶模型实现一维栅格成像恢复.该方法采用数学形态法中的腐蚀算法生成栅格点云数据，通过U-Net网络源模型从点云中提取针尖卷积效应的特征向量，将权重参数迁移至U-Net网络靶模型，靶模型在自适应正则化方法下进行监督学习.实验结果表明，该方法能有效恢复一维栅格的原子力显微镜测量图像，提高横向分辨力，可用于纳米栅格的线宽检测上.

Due to the structure size of the atomic force microscope probe tip, image edge distortion will occur when micro-nano measurement is performed. Thus, a blind restoration method of atomic force microscopy image based on transfer learning is proposed, where the blind restoration for the one-dimensional raster image can be realized by training sourcing model and target model. This method uses the corrosion algorithm of mathematical morphology to generate grid training samples, extracts the characteristic vectors of the convolution effect from the samples by applying the U-Net network source model, where the weight parameters are migrated to the U-Net network target model. Then the source model can conduct supervised learning under adaptive regularization method. The experimental results show that the proposed method can effectively restore the atomic force microscopy measurement image of one-dimensional grid, improve the lateral resolution, and be used in the linewidth detection of nano grid.