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arXiv · 2603.19187

GenMFSR: Generative Multi-Frame Image Restoration and Super-Resolution

Harshana Weligampola·Joshua Peter Ebenezer·Weidi Liu·Abhinau K. Venkataramanan·Sreenithy Chandran·Seok-Jun Lee·Hamid Rahim Sheikh
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Abstract

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.

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Harshana Weligampola, Joshua Peter Ebenezer, Weidi Liu, Abhinau K. Venkataramanan, Sreenithy Chandran, Seok-Jun Lee, Hamid Rahim Sheikh. 2026-03-19. GenMFSR: Generative Multi-Frame Image Restoration and Super-Resolution. https://arxiv.org/abs/2603.19187

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