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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/2510.09410 |
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| _version_ | 1866914086927728640 |
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| author | Yokoyama, Yuichi Yamagami, Kohei Sumiya, Yuta Shouno, Hayaru Mizumaki, Masaichiro |
| author_facet | Yokoyama, Yuichi Yamagami, Kohei Sumiya, Yuta Shouno, Hayaru Mizumaki, Masaichiro |
| contents | Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectiveness by removing grid artifacts in angle-resolved photoemission spectroscopy (ARPES), a long-standing and critical data analysis challenge in materials science. Our results demonstrate that deep prior-based denoising yields clearer ARPES images in a fraction of the time required by conventional, experiment-based denoising methods. This ultra-efficient approach to ARPES will enable high-speed, high-resolution three-dimensional band structure mapping in momentum space, thereby dramatically accelerating our understanding of microscopic electronic structures of materials. Beyond ARPES, deep prior-based denoising represents a versatile tool that could become a new standard in any advanced scientific measurement fields where data acquisition is limited. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep prior-based denoising for state-of-the-art scientific imaging and metrology Yokoyama, Yuichi Yamagami, Kohei Sumiya, Yuta Shouno, Hayaru Mizumaki, Masaichiro Materials Science Deep learning has revolutionized computer vision, yet a major gap persists between complex, data-hungry deep learning models and the practical demands of state-of-the-art scientific measurements. To fundamentally bridge this gap, we propose deep prior-based denoising, a robust deep learning model that requires no training data. We demonstrate its effectiveness by removing grid artifacts in angle-resolved photoemission spectroscopy (ARPES), a long-standing and critical data analysis challenge in materials science. Our results demonstrate that deep prior-based denoising yields clearer ARPES images in a fraction of the time required by conventional, experiment-based denoising methods. This ultra-efficient approach to ARPES will enable high-speed, high-resolution three-dimensional band structure mapping in momentum space, thereby dramatically accelerating our understanding of microscopic electronic structures of materials. Beyond ARPES, deep prior-based denoising represents a versatile tool that could become a new standard in any advanced scientific measurement fields where data acquisition is limited. |
| title | Deep prior-based denoising for state-of-the-art scientific imaging and metrology |
| topic | Materials Science |
| url | https://arxiv.org/abs/2510.09410 |