Noise Calibration: Plug-and-play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models

In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining content consistency between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and content consistency. Content consistency is ensured by a proposed loss fuction that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, which we refer to as \textbf{Noise Calibration}. By simply refining the initial random noise through a few iterations, the original video content can be largely preserved, and the enhancement effect is significantly improved. Extensive experiments have demonstrated the effectiveness of the proposed method.