Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach
This provides a robust, geometry-aware solution for high-fidelity AFM data interpretation, addressing a domain-specific problem for researchers in nanotechnology and materials science.
The paper tackled artifact removal in Atomic Force Microscopy (AFM) images by developing an automated framework that detects and restores artifacts using a structured mask-guided directional inpainting approach, resulting in effective artifact removal while preserving nanoscale structural details.
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.