从 artefact 到洞察：为扫描探针显微镜图像修复的高效低秩适应 BrushNet
From Artefact to Insight: Efficient Low-Rank Adaptation of BrushNet for Scanning Probe Microscopy Image Restoration
AI总结：
本文提出基于扩散的修复框架，通过低秩适应对BrushNet进行微调，有效去除SPM图像中的结构化artefacts，提升PSNR并降低LPIPS，实现高效图像修复。
摘要：
扫描探针显微镜（SPM）提供纳米级分辨率，但常受结构化artefacts如线扫描丢失、增益诱导噪声、探针卷积和相位跳跃影响。现有方法多将artefact去除视为去噪或插值任务，而生成式修补视角尚待探索。本文引入基于扩散的修补框架，通过低秩适应对BrushNet进行微调，仅需7390个artefact与清洁配对，即可在SPM InpBench基准上提升PSNR 6.61 dB，降低LPIPS至零样本推理的一半，且在单GPU上实现与全重训练相当的精度。该方法可跨多种SPM图像通道（高度、振幅和相位）通用，忠实恢复细微结构细节，并抑制来自自然图像先验的hallucination artefacts。此轻量级框架实现了对不可替代SPM图像的高效、可扩展恢复，为扩散模型在纳观成像分析中的应用铺平道路。
英文摘要：
Scanning Probe Microscopy or SPM offers nanoscale resolution but is frequently marred by structured artefacts such as line scan dropout, gain induced noise, tip convolution, and phase hops. While most available methods treat SPM artefact removal as isolated denoising or interpolation tasks, the generative inpainting perspective remains largely unexplored. In this work, we introduce a diffusion based inpainting framework tailored to scientific grayscale imagery. By fine tuning less than 0.2 percent of BrushNet weights with rank constrained low rank adaptation (LoRA), we adapt a pretrained diffusion model using only 7390 artefact, clean pairs distilled from 739 experimental scans. On our forthcoming public SPM InpBench benchmark, the LoRA enhanced model lifts the Peak Signal to Noise Ratio or PSNR by 6.61 dB and halves the Learned Perceptual Image Patch Similarity or LPIPS relative to zero-shot inference, while matching or slightly surpassing the accuracy of full retraining, trainable on a single GPU instead of four high-memory cards. The approach generalizes across various SPM image channels including height, amplitude and phase, faithfully restores subtle structural details, and suppresses hallucination artefacts inherited from natural image priors. This lightweight framework enables efficient, scalable recovery of irreplaceable SPM images and paves the way for a broader diffusion model adoption in nanoscopic imaging analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
