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
{https://github.com/yuanzhi-zhu/DiffPIR}

本文提出了 DiffPIR，将传统的 plug-and-play 方法集成到扩散采样框架中，以期在保留扩散模型生成能力的同时完成图像还原任务，实验结果表明，DiffPIR 在三项代表性图像还原任务上取得了最先进的性能。

用于即插即用图像恢复的去噪扩散模型

Denoising Diffusion Models for Plug-and-Play Image Restoration

Recent works on plug-and-play image restoration have shown that a denoiser
can implicitly serve as the image prior for model-based methods to solve many
inverse problems. Such a property induces considerable advantages for
plug-and-play image restoration (e.g., integrating the flexibility of
model-based method and effectiveness of learning-based methods) when the
denoiser is discriminatively learned via deep convolutional neural network
(CNN) with large modeling capacity. However, while deeper and larger CNN models
are rapidly gaining popularity, existing plug-and-play image restoration
hinders its performance due to the lack of suitable denoiser prior. In order to
push the limits of plug-and-play image restoration, we set up a benchmark deep
denoiser prior by training a highly flexible and effective CNN denoiser. We
then plug the deep denoiser prior as a modular part into a half quadratic
splitting based iterative algorithm to solve various image restoration
problems. We, meanwhile, provide a thorough analysis of parameter setting,
intermediate results and empirical convergence to better understand the working
mechanism. Experimental results on three representative image restoration
tasks, including deblurring, super-resolution and demosaicing, demonstrate that
the proposed plug-and-play image restoration with deep denoiser prior not only
significantly outperforms other state-of-the-art model-based methods but also
achieves competitive or even superior performance against state-of-the-art
learning-based methods. The source code is available at
this https URL

本研究旨在通过训练高度灵活和有效的深度 CNN 降噪器来构建深度降噪先验，将其作为模块化部分插入基于半二次分裂的迭代算法以解决各种图像恢复问题，实验结果表明所提出的方案不仅对于模型模拟的方法具有优势，对于学习方法也具有竞争性甚至更好的性能。

使用深度去噪先验的即插即用图像恢复

Plug-and-Play Image Restoration with Deep Denoiser Prior

While deep neural networks (DNN) based single image super-resolution (SISR)
methods are rapidly gaining popularity, they are mainly designed for the
widely-used bicubic degradation, and there still remains the fundamental
challenge for them to super-resolve low-resolution (LR) image with arbitrary
blur kernels. In the meanwhile, plug-and-play image restoration has been
recognized with high flexibility due to its modular structure for easy plug-in
of denoiser priors. In this paper, we propose a principled formulation and
framework by extending bicubic degradation based deep SISR with the help of
plug-and-play framework to handle LR images with arbitrary blur kernels.
Specifically, we design a new SISR degradation model so as to take advantage of
existing blind deblurring methods for blur kernel estimation. To optimize the
new degradation induced energy function, we then derive a plug-and-play
algorithm via variable splitting technique, which allows us to plug any
super-resolver prior rather than the denoiser prior as a modular part.
Quantitative and qualitative evaluations on synthetic and real LR images
demonstrate that the proposed deep plug-and-play super-resolution framework is
flexible and effective to deal with blurry LR images.

本文提出了一种基于深度神经网络和插拔式图像恢复技术相结合的原理性公式和框架，使得任意位于低分辨率图像的模糊核能够被处理，为此设计了一个新的图像降级模型，并基于该模型提出一个基于变量分割技术的插拔式算法，实现了预处理和任意大小和类型的超分辨率。