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

Source: [Artifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach](<https://www.sota2.com/research/paper/69842404a8b01e76cc8ee6d9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-06T05:00:04.455Z

Authors: Juntao Zhang, Angona Biswas, Jaydeep Rade, Charchit Shukla, Juan Ren, Anwesha Sarkar, Adarsh Krishnamurthy, Aditya Balu

Published: 2026-02-03T22:37:22.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.04051>)

[PDF](<https://arxiv.org/pdf/2602.04051.pdf>)