HaineiFRDM: Explore Diffusion to Restore Defects in Fast-Movement Films

• URL: http://arxiv.org/abs/2512.24946v1
• Date: Wed, 31 Dec 2025 16:18:07 GMT
• Title: HaineiFRDM: Explore Diffusion to Restore Defects in Fast-Movement Films
• Authors: Rongji Xun, Junjie Yuan, Zhongjie Wang,
• Abstract summary: Existing open-source film restoration methods show limited performance compared to commercial methods.<n>We propose HaineiFRDM, a film restoration framework, to explore diffusion model's powerful content-understanding ability.
• Score: 2.3374825727995328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing open-source film restoration methods show limited performance compared to commercial methods due to training with low-quality synthetic data and employing noisy optical flows. In addition, high-resolution films have not been explored by the open-source methods.We propose HaineiFRDM(Film Restoration Diffusion Model), a film restoration framework, to explore diffusion model's powerful content-understanding ability to help human expert better restore indistinguishable film defects.Specifically, we employ a patch-wise training and testing strategy to make restoring high-resolution films on one 24GB-VRAMR GPU possible and design a position-aware Global Prompt and Frame Fusion Modules.Also, we introduce a global-local frequency module to reconstruct consistent textures among different patches. Besides, we firstly restore a low-resolution result and use it as global residual to mitigate blocky artifacts caused by patching process.Furthermore, we construct a film restoration dataset that contains restored real-degraded films and realistic synthetic data.Comprehensive experimental results conclusively demonstrate the superiority of our model in defect restoration ability over existing open-source methods. Code and the dataset will be released.

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