Abstract
Atomic Force Microscopy (AFM) enables high-resolution surface imaging at the nanoscale, yet the output is often degraded by artifacts introduced by environmental noise, scanning imperfections, and tip-sample interactions. To address this challenge, a lightweight and fully automated framework for artifact detection and restoration in AFM image analysis is presented. The pipeline begins with a classification model that determines whether an AFM image contains artifacts. If necessary, a lightweight semantic segmentation network, custom-designed and trained on AFM data, is applied to generate precise artifact masks. These masks are adaptively expanded based on their structural orientation and then inpainted using a directional neighbor-based interpolation strategy to preserve 3D surface continuity. A localized Gaussian smoothing operation is then applied for seamless restoration. The system is integrated into a user-friendly GUI that supports real-time parameter adjustments and batch processing. Experimental results demonstrate the effective artifact removal while preserving nanoscale structural details, providing a robust, geometry-aware solution for high-fidelity AFM data interpretation.
Abstract (translated)
原子力显微镜（AFM）能够在纳米尺度上实现高分辨率表面成像，然而输出结果常因环境噪声、扫描缺陷和探针-样品相互作用等因素而退化。为解决这一挑战，本文提出了一种轻量级且完全自动化的框架，用于在AFM图像分析中检测并修复这些伪影。该流程首先使用分类模型判断一张AFM图是否含有伪影。如果需要，会应用一种基于AFM数据定制设计和训练的轻量化语义分割网络，生成精确的伪影掩模。根据其结构方向动态扩展这些掩模后，采用基于方向邻居插值策略进行修复以保持三维表面连续性，并通过局部高斯平滑操作实现无缝修复。 该系统集成于一个用户友好的图形界面（GUI）中，支持实时参数调整和批量处理功能。实验结果表明，此方法能够有效地移除伪影同时保留纳米级结构细节，为AFM数据的高保真解读提供了一种稳健且具有几何感知能力的解决方案。
URL
https://arxiv.org/abs/2602.04051
