Recently, various methods have been proposed to solve Image Restoration (IR)
tasks using a pre-trained diffusion model leading to state-of-the-art
performance. However, most of these methods assume that the degradation
operator in the IR task is completely known. Furthermore, a common
characteristic among these approaches is that they alter the diffusion sampling
process in order to satisfy the consistency with the degraded input image. This
choice has recently been shown to be sub-optimal and to cause the restored
image to deviate from the data manifold. To address these issues, we propose
Blind Image Restoration via fast Diffusion inversion (BIRD) a blind IR method
that jointly optimizes for the degradation model parameters and the restored
image. To ensure that the restored images lie onto the data manifold, we
propose a novel sampling technique on a pre-trained diffusion model. A key idea
in our method is not to modify the reverse sampling, i.e., not to alter all the
intermediate latents, once an initial noise is sampled. This is ultimately
equivalent to casting the IR task as an optimization problem in the space of
the input noise. Moreover, to mitigate the computational cost associated with
inverting a fully unrolled diffusion model, we leverage the inherent capability
of these models to skip ahead in the forward diffusion process using large time
steps. We experimentally validate BIRD on several image restoration tasks and
show that it achieves state of the art performance on all of them. Our code is
available at this https URL

通过使用预训练的扩散模型，我们提出了一种盲图像恢复方法 BIRD，它在优化退化模型参数和恢复图像的同时，通过在数据流形上提出新的采样技术，将图像恢复任务转化为输入噪声空间中的优化问题。实验证明，该方法在多个图像恢复任务上实现了最先进的性能。

通过快速扩散反演进行盲图像恢复

Blind Image Restoration via Fast Diffusion Inversion

Learning-based methods for blind single image super resolution (SISR) conduct
the restoration by a learned mapping between high-resolution (HR) images and
their low-resolution (LR) counterparts degraded with arbitrary blur kernels.
However, these methods mostly require an independent step to estimate the blur
kernel, leading to error accumulation between steps. We propose an end-to-end
learning framework for the blind SISR problem, which enables image restoration
within a unified Bayesian framework with either full- or semi-supervision. The
proposed method, namely SREMN, integrates learning techniques into the
generalized expectation-maximization (GEM) algorithm and infers HR images from
the maximum likelihood estimation (MLE). Extensive experiments show the
superiority of the proposed method with comparison to existing work and novelty
in semi-supervised learning.

本文提出了一种基于学习方法的针对盲目全像单幅图像超分辨率重建的端到端学习框架，该方法将学习技术集成到广义 EM 算法中，通过最大似然估计从低分辨率图像中推断出高分辨率图像，并在半监督学习中具有较高的性能。