✨FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution

📝 Summary:
FlashVSR introduces the first real-time, one-step streaming diffusion framework for video super-resolution. It addresses high latency and computation through innovations like distillation, sparse attention, and a tiny decoder. FlashVSR achieves state-of-the-art performance with up to 12x speedup.

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.12747
• PDF: https://arxiv.org/pdf/2510.12747
• Project Page: https://zhuang2002.github.io/FlashVSR/
• Github: https://github.com/OpenImagingLab/FlashVSR

🔹 Models citing this paper:
• https://huggingface.co/JunhaoZhuang/FlashVSR
• https://huggingface.co/JunhaoZhuang/FlashVSR-v1.1

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#FlashVSR #VideoSuperResolution #RealTimeAI #DiffusionModels #ComputerVision

✨FMA-Net++: Motion- and Exposure-Aware Real-World Joint Video Super-Resolution and Deblurring

📝 Summary:
FMA-Net++ addresses joint video super-resolution and deblurring by modeling motion and dynamic exposure. It employs an exposure-aware sequence architecture, decoupling degradation learning from restoration for top accuracy and efficiency.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04390
• PDF: https://arxiv.org/pdf/2512.04390
• Project Page: https://kaist-viclab.github.io/fmanetpp_site/
• Github: https://kaist-viclab.github.io/fmanetpp_site/

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For more data science resources:
✓ https://t.me/DataScienceT

#VideoSuperResolution #VideoDeblurring #ComputerVision #DeepLearning #ImageProcessing