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arXiv · 2602.04051

Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach

Juntao Zhang·Angona Biswas·Jaydeep Rade·Charchit Shukla·Juan Ren·Anwesha Sarkar·Adarsh Krishnamurthy·Aditya Balu
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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.

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Juntao Zhang, Angona Biswas, Jaydeep Rade, Charchit Shukla, Juan Ren, Anwesha Sarkar, Adarsh Krishnamurthy, Aditya Balu. 2026-02-03. Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach. https://arxiv.org/abs/2602.04051

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