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

TL;DR

This paper introduces a fully automated, lightweight framework for detecting, segmenting, and restoring artifacts in AFM images, enhancing image quality while preserving nanoscale details through a structured directional inpainting approach.

Contribution

The authors propose a novel, integrated pipeline combining artifact detection, custom segmentation, adaptive mask expansion, and directional inpainting tailored for AFM images, improving restoration accuracy.

Findings

Effective artifact removal demonstrated on AFM data.

Preserves nanoscale surface details during restoration.

Supports real-time processing and batch analysis.

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.