Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913836331696128 |
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| author | Xu, Wuzhe Lu, Yulong Wang, Sifan Liu, Tong-Rui |
| author_facet | Xu, Wuzhe Lu, Yulong Wang, Sifan Liu, Tong-Rui |
| contents | We propose a unified diffusion model-based correction and super-resolution method to enhance the fidelity and resolution of diverse low-quality data through a two-step pipeline. First, the correction step employs a novel enhanced stochastic differential editing technique based on an imbalanced perturbation and denoising process, ensuring robust and effective bias correction at the low-resolution level. The robustness and effectiveness of this approach are validated theoretically and experimentally. Next, the super-resolution step leverages cascaded conditional diffusion models to iteratively refine the corrected data to high-resolution. Numerical experiments on three PDE problems and a climate dataset demonstrate that the proposed method effectively enhances low-fidelity, low-resolution data by correcting numerical errors and noise while simultaneously improving resolution to recover fine-scale structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution Xu, Wuzhe Lu, Yulong Wang, Sifan Liu, Tong-Rui Numerical Analysis We propose a unified diffusion model-based correction and super-resolution method to enhance the fidelity and resolution of diverse low-quality data through a two-step pipeline. First, the correction step employs a novel enhanced stochastic differential editing technique based on an imbalanced perturbation and denoising process, ensuring robust and effective bias correction at the low-resolution level. The robustness and effectiveness of this approach are validated theoretically and experimentally. Next, the super-resolution step leverages cascaded conditional diffusion models to iteratively refine the corrected data to high-resolution. Numerical experiments on three PDE problems and a climate dataset demonstrate that the proposed method effectively enhances low-fidelity, low-resolution data by correcting numerical errors and noise while simultaneously improving resolution to recover fine-scale structures. |
| title | Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2505.08526 |