DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models (2407.01519v4)

Abstract: We present DiffIR2VR-Zero, a zero-shot framework that enables any pre-trained image restoration diffusion model to perform high-quality video restoration without additional training. While image diffusion models have shown remarkable restoration capabilities, their direct application to video leads to temporal inconsistencies, and existing video restoration methods require extensive retraining for different degradation types. Our approach addresses these challenges through two key innovations: a hierarchical latent warping strategy that maintains consistency across both keyframes and local frames, and a hybrid token merging mechanism that adaptively combines optical flow and feature matching. Through extensive experiments, we demonstrate that our method not only maintains the high-quality restoration of base diffusion models but also achieves superior temporal consistency across diverse datasets and degradation conditions, including challenging scenarios like 8$\times$ super-resolution and severe noise. Importantly, our framework works with any image restoration diffusion model, providing a versatile solution for video enhancement without task-specific training or modifications.

• The paper introduces a zero-shot video restoration framework that adapts pre-trained diffusion models to diverse degradation scenarios without retraining.
• It employs a novel two-pronged strategy combining hierarchical latent warping and hybrid flow-guided spatial token merging to maintain consistent video quality.
• Empirical evaluations on datasets such as SPMCS, DAVIS, and REDS30 show significant improvements in PSNR, SSIM, and LPIPS over conventional methods.

Zero-Shot Video Restoration with Diffusion Models: A Critical Overview of DiffIR2VR-Zero

In the paper "DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models," the authors present a sophisticated approach to video restoration that eliminates the reliance on extensive training datasets and the need for retraining models specifically tailored to different degradation scenarios. The proposed method utilizes pre-trained image restoration diffusion models, employing them in a novel framework to achieve zero-shot video restoration. This is a significant methodological shift away from traditional convolutional neural networks (CNNs) and transformer-based methods that often demand large-scale data and lead to results confined by the scope of their training datasets.

Methodological Advances

The paper introduces a two-pronged strategy combining a hierarchical token merging for keyframes and a hybrid correspondence mechanism that includes optical flow and feature-based nearest neighbor matching (latent merging). This design enhances temporal consistency in videos by ensuring that both global and local latent structures are maintained throughout frames without altering the core architecture of the pre-trained models. The framework is characterized by two primary modules:

1. Hierarchical Latent Warping: This component offers rough shape guidance by propagating latents across both keyframes and within frame batches, ensuring the temporal coherence of restored videos.
2. Hybrid Flow-Guided Spatial-Aware Token Merging: This module matches tokens across frames, employing both spatial awareness and optical flow guidance, which significantly improves temporal consistency while maintaining the video quality.

These modules allow for manipulation within both latent and token spaces to enforce semantic consistency across video frames.

Empirical Performance

Quantitative assessments demonstrate that the method significantly outperforms both traditional and other modern approaches under varied degradation scenarios, such as 8× super-resolution and high-standard deviation noise conditions. The method is tested against challenging datasets like SPMCS and DAVIS for video super-resolution and REDS30 for video denoising. The results reveal consistent improvements in metrics like PSNR, SSIM, and LPIPS, indicating not only superior restoration quality but also enhanced temporal consistency. Particularly noteworthy is the model's ability to handle severe degradation scenarios where conventional methods often fail.

Implications and Future Directions

The paper proposes a promising direction for video restoration, offering a framework that is adaptable to any pre-trained image diffusion model without additional training. This adaptability is particularly advantageous in practical applications where computational resources are limited or retraining is impractical. The approach lays the groundwork for more generalized solutions in video enhancement tasks and could influence a broad range of fields requiring high-quality video outputs, such as surveillance, healthcare imaging, and entertainment.

Future research might explore optimizing keyframe selection to improve restoration especially under severe degradations and further stabilizing the diffusion-based outcomes to minimize flickering in dynamic scenes. As the architecture permits application to various pre-trained models, exploration with different diffusion architectures could yield further performance enhancements or unlock novel applications.

In summary, DiffIR2VR-Zero introduces a versatile and robust framework for zero-shot video restoration that can potentially redefine current paradigms in video processing using diffusion models. The method's ability to operate effectively across diverse scenarios without specialized training poses a significant step towards flexible and efficient video enhancement solutions.