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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.16951 |
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| _version_ | 1866916924381724672 |
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| author | Falaleev, Nikolay Orlov, Nikolai |
| author_facet | Falaleev, Nikolay Orlov, Nikolai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16951 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DIFFRACT: Diffusion-based Restoration via Adaptive Control and Thresholding for Diffraction Imaging Falaleev, Nikolay Orlov, Nikolai Image and Video Processing 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. |
| title | DIFFRACT: Diffusion-based Restoration via Adaptive Control and Thresholding for Diffraction Imaging |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2504.16951 |