Zero-Shot Video Restoration and Enhancement with Assistance of Video Diffusion Models
This addresses video quality issues for applications like media processing, though it is incremental as it builds on existing diffusion-based image methods.
The paper tackles the problem of temporal flickering in zero-shot video restoration and enhancement by proposing a framework that uses video diffusion models to assist image-based methods, achieving improved temporal consistency without training.
Although diffusion-based zero-shot image restoration and enhancement methods have achieved great success, applying them to video restoration or enhancement will lead to severe temporal flickering. In this paper, we propose the first framework that utilizes the rapidly-developed video diffusion model to assist the image-based method in maintaining more temporal consistency for zero-shot video restoration and enhancement. We propose homologous latents fusion, heterogenous latents fusion, and a COT-based fusion ratio strategy to utilize both homologous and heterogenous text-to-video diffusion models to complement the image method. Moreover, we propose temporal-strengthening post-processing to utilize the image-to-video diffusion model to further improve temporal consistency. Our method is training-free and can be applied to any diffusion-based image restoration and enhancement methods. Experimental results demonstrate the superiority of the proposed method.