Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach
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.
Cite
@article{arxiv.2602.04051,
title = {Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach},
author = {Juntao Zhang and Angona Biswas and Jaydeep Rade and Charchit Shukla and Juan Ren and Anwesha Sarkar and Adarsh Krishnamurthy and Aditya Balu},
journal= {arXiv preprint arXiv:2602.04051},
year = {2026}
}