VDFP uses degradation field modeling based on rolling shutter and continuous prior perception with a flicker-aware loss to deflicker videos while preserving spatial-temporal details via zero-initialized pre-trained priors.
Dove: Efficient one-step diffusion model for real-world video super-resolution
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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DiffST delivers state-of-the-art real-world space-time video super-resolution with 17x faster inference than prior diffusion methods by using one-step sampling, cross-frame context aggregation, and video representation guidance.
citing papers explorer
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VDFP: Video Deflickering with Flicker-banding Priors
VDFP uses degradation field modeling based on rolling shutter and continuous prior perception with a flicker-aware loss to deflicker videos while preserving spatial-temporal details via zero-initialized pre-trained priors.
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DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
DiffST delivers state-of-the-art real-world space-time video super-resolution with 17x faster inference than prior diffusion methods by using one-step sampling, cross-frame context aggregation, and video representation guidance.