Denoising Diffusion Models for Plug-and-Play Image Restoration (2305.08995v1)

Abstract: Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior. However, most existing methods focus on discriminative Gaussian denoisers. Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored. While several other attempts have been made to adopt diffusion models for image restoration, they either fail to achieve satisfactory results or typically require an unacceptable number of Neural Function Evaluations (NFEs) during inference. This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework. Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models. Experimental results on three representative IR tasks, including super-resolution, image deblurring, and inpainting, demonstrate that DiffPIR achieves state-of-the-art performance on both the FFHQ and ImageNet datasets in terms of reconstruction faithfulness and perceptual quality with no more than 100 NFEs. The source code is available at {\url{https://github.com/yuanzhi-zhu/DiffPIR}}

• The paper introduces DiffPIR, a method that replaces traditional Gaussian denoisers with generative diffusion models to effectively tackle complex inverse problems.
• It integrates iterative ADMM/HQS optimization and a novel sampling strategy to refine image estimates, achieving high-quality reconstructions on benchmarks like FFHQ and ImageNet.
• DiffPIR demonstrates superior perceptual quality and flexibility, paving the way for future research in efficient and versatile image restoration applications.

Denoising Diffusion Models for Plug-and-Play Image Restoration: An Expert Overview

Introduction

The paper, "Denoising Diffusion Models for Plug-and-Play Image Restoration," explores the integration of denoising diffusion models with plug-and-play image restoration frameworks. The proposed method, DiffPIR, seeks to address limitations in current IR techniques that rely heavily on discriminative Gaussian denoisers. By leveraging the generative capabilities of diffusion models, this approach aims to enhance the flexibility and performance of image restoration tasks.

Methodological Advancements

DiffPIR builds upon the foundational plug-and-play IR approach, which separates the data and prior terms in optimization problems. Traditional methods harness discriminative Gaussian denoisers, but the authors propose substituting these with diffusion models, which encapsulate generative power and can model complex data distributions more effectively.

Core Components:

1. Diffusion Models as Denoisers: By using diffusion models as generative denoisers, DiffPIR can better handle ill-posed inverse problems, benefiting from the models' ability to synthesize high-quality images.
2. Iterative Solution: The paper integrates the Alternating Direction Method of Multipliers (ADMM) and Half-Quadratic-Splitting (HQS) to iteratively solve optimization subproblems. This process involves decoupling data fidelity and prior terms, allowing for efficient application of generative priors.
3. Sampling Algorithm: DiffPIR employs a novel sampling strategy within the diffusion framework, iteratively refining image estimates and integrating measurement data to achieve high-quality reconstructions.

Experiments and Results

The experimental evaluation of DiffPIR is comprehensive, involving several benchmarks on image restoration tasks such as super-resolution (SR), deblurring, and inpainting across FFHQ and ImageNet datasets. Quantitative metrics such as PSNR, FID, and LPIPS are utilized to assess performance.

Key Findings:

1. Superior Perceptual Quality: DiffPIR achieves state-of-the-art results, especially in terms of FID and LPIPS, demonstrating its capability to produce images with high perceptual quality even with fewer Neural Function Evaluations (NFEs) (≤ 100).
2. Flexibility and Efficiency: The method offers notable advantages in handling diverse and complex degradation models, such as motion blur and arbitrary inpainting masks, proving its versatility.
3. Theoretical and Practical Implications: The effective use of diffusion models as generative priors can potentially inspire further exploration into their application for broader image processing tasks, leveraging their inherent capabilities to model intricate data distributions.

Implications and Future Directions

DiffPIR represents a significant step forward in integrating generative models into the image restoration pipeline, broadening the scope of plug-and-play methods. The combination of high-quality image synthesis with efficient sampling mechanisms paves the way for more practical applications in computational photography and vision.

Future Developments:

• Enhanced Algorithms: Further research could explore algorithmic optimization to reduce computational overhead and improve real-time applicability.
• Broader Applications: The methodology could be extended to other domains such as video restoration or 3D reconstruction, exploiting the generative strengths of diffusion models in dynamic contexts.
• Hybrid Systems: Integration with other machine learning frameworks may also enhance performance by coupling diffusion capabilities with domain-specific knowledge.

In sum, DiffPIR exemplifies a sophisticated approach to image restoration by merging diffusion models into the plug-and-play framework, yielding promising results that could reshape future research and application landscapes in AI-driven image processing.