用定向填充法自动去除原子力显微镜图像中的伪影，保留纳米结构细节。
Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach
- 基于结构掩码的定向插值，自适应扩展伪影区域并修复表面连续性。
- 在真实AFM数据上实现伪影消除，同时保留纳米级结构特征。
- 轻量级全自动系统，支持实时调整与批量处理，适合材料科研人员使用。
原子力显微镜（AFM）可在纳米尺度实现高分辨率表面成像，但常受环境噪声、扫描误差及探针-样品相互作用引入的伪影影响。本文提出一种轻量级、全自动的AFM图像伪影检测与修复框架。首先通过分类模型判断图像是否含伪影；若有，则使用定制训练的轻量语义分割网络生成精确伪影掩码。掩码根据结构方向自适应扩展，并采用基于方向邻域的插值策略进行填充，以保持三维表面连续性。随后应用局部高斯平滑实现无缝修复。系统集成于用户友好的图形界面，支持实时参数调节与批量处理。实验表明，该方法能有效去除伪影，同时保留纳米级结构细节，为高保真AFM数据分析提供几何感知解决方案。
原文摘要 · Abstract (English)
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.
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