原子力显微镜中的瑕疵去除与图像修复：一种结构掩膜引导的定向修复方法
Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach
AI总结：
本文提出了一种基于结构掩膜引导的定向修复方法，用于原子力显微镜图像中瑕疵的自动检测与修复，通过语义分割和方向插值技术提升纳米级图像质量。
摘要：
原子力显微镜（AFM）能够实现纳米尺度的高分辨率表面成像，但输出常因环境噪声、扫描不完美和探针-样品相互作用引入的瑕疵而被降质。为解决这一挑战，本文提出了一种轻量且完全自动化的框架，用于AFM图像分析中的瑕疵检测与修复。该流程首先通过分类模型判断AFM图像是否包含瑕疵。如果需要，会应用一个轻量级语义分割网络，该网络是基于AFM数据定制设计并训练的，用于生成精确的瑕疵掩膜。这些掩膜会根据其结构方向进行自适应扩展，然后使用基于方向邻居的插值策略进行修复，以保持三维表面连续性。随后，应用局部高斯平滑操作以实现无缝修复。该系统集成到一个用户友好的GUI中，支持实时参数调整和批量处理。实验结果表明，该方法能有效去除瑕疵，同时保留纳米级结构细节，提供了一种稳健、几何感知的高保真AFM数据解释解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
