用扩散模型自适应去噪电子背散射衍射图，防幻觉且可调进度
DIFFRACT: Diffusion-based Restoration via Adaptive Control and Thresholding for Diffraction Imaging
- 分两阶段训练，用UNet加质量预测头实现反馈控制
- 自适应调节去噪流程，提升图像质量并避免无效修复
- 能识别无信号样本，适合材料表征中高噪声数据处理
本文提出一种基于扩散模型的电子背散射衍射（EBSD）图案去噪新方法。采用两阶段训练的UNet架构，引入辅助回归头预测实验图案质量，评估去噪进展。模型通过质量预测与反馈驱动的迭代去噪过程实现自适应控制，动态调整去噪节奏，实现精细调控。此外，模型可识别无有效信号的样本，降低幻觉风险。我们构建了包含EBSD图案、对应母图（Master Patterns）及质量值的定制数据集，并成功验证了DIFFRACT在实际应用中的有效性。
原文摘要 · Abstract (English)
This paper presents a novel approach for denoising Electron Backscatter Diffraction (EBSD) patterns using diffusion models. We propose a two-stage training process with a UNet-based architecture, incorporating an auxiliary regression head to predict the quality of the experimental pattern and assess the progress of the denoising process. The model uses an adaptive denoising strategy, which integrates quality prediction and feedback-driven iterative denoising process control. This adaptive feedback loop allows the model to adjust its schedule, providing fine control over the denoising process. Furthermore, our model can identify samples where no meaningful signal is present, thereby reducing the risk of hallucinations. We demonstrate the DIFFRACT - the successful application of diffusion models to EBSD pattern denoising using a custom-collected dataset of EBSD patterns, their corresponding Master Patterns, and quality values.
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