GenMFSR: Generative Multi-Frame Image Restoration and Super-Resolution
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912974569996288 |
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| author | Weligampola, Harshana Ebenezer, Joshua Peter Liu, Weidi Venkataramanan, Abhinau K. Chandran, Sreenithy Lee, Seok-Jun Sheikh, Hamid Rahim |
| author_facet | Weligampola, Harshana Ebenezer, Joshua Peter Liu, Weidi Venkataramanan, Abhinau K. Chandran, Sreenithy Lee, Seok-Jun Sheikh, Hamid Rahim |
| contents | Camera pipelines receive raw Bayer-format frames that need to be denoised, demosaiced, and often super-resolved. Multiple frames are captured to utilize natural hand tremors and enhance resolution. Multi-frame super-resolution is therefore a fundamental problem in camera pipelines. Existing adversarial methods are constrained by the quality of ground truth. We propose GenMFSR, the first Generative Multi-Frame Raw-to-RGB Super Resolution pipeline, that incorporates image priors from foundation models to obtain sub-pixel information for camera ISP applications. GenMFSR can align multiple raw frames, unlike existing single-frame super-resolution methods, and we propose a loss term that restricts generation to high-frequency regions in the raw domain, thus preventing low-frequency artifacts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19187 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GenMFSR: Generative Multi-Frame Image Restoration and Super-Resolution Weligampola, Harshana Ebenezer, Joshua Peter Liu, Weidi Venkataramanan, Abhinau K. Chandran, Sreenithy Lee, Seok-Jun Sheikh, Hamid Rahim Image and Video Processing Camera pipelines receive raw Bayer-format frames that need to be denoised, demosaiced, and often super-resolved. Multiple frames are captured to utilize natural hand tremors and enhance resolution. Multi-frame super-resolution is therefore a fundamental problem in camera pipelines. Existing adversarial methods are constrained by the quality of ground truth. We propose GenMFSR, the first Generative Multi-Frame Raw-to-RGB Super Resolution pipeline, that incorporates image priors from foundation models to obtain sub-pixel information for camera ISP applications. GenMFSR can align multiple raw frames, unlike existing single-frame super-resolution methods, and we propose a loss term that restricts generation to high-frequency regions in the raw domain, thus preventing low-frequency artifacts. |
| title | GenMFSR: Generative Multi-Frame Image Restoration and Super-Resolution |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2603.19187 |