Two of the main challenges of image restoration in real-world scenarios are the accurate characterization of an image prior and the precise modeling of the image degradation operator. Pre-trained diffusion models have been very successfully used as image priors in zero-shot image restoration methods. However, how to best handle the degradation operator is still an open problem. In real-world data, methods that rely on specific parametric assumptions about the degradation model often face limitations in their applicability. To address this, we introduce Invert2Restore, a zero-shot, training-free method that operates in both fully blind and partially blind settings -- requiring no prior knowledge of the degradation model or only partial knowledge of its parametric form without known parameters. Despite this, Invert2Restore achieves high-fidelity results and generalizes well across various types of image degradation. It leverages a pre-trained diffusion model as a deterministic mapping between normal samples and undistorted image samples. The key insight is that the input noise mapped by a diffusion model to a degraded image lies in a low-probability density region of the standard normal distribution. Thus, we can restore the degraded image by carefully guiding its input noise toward a higher-density region. We experimentally validate Invert2Restore across several image restoration tasks, demonstrating that it achieves state-of-the-art performance in scenarios where the degradation operator is either unknown or partially known.

翻译：现实场景中图像复原面临的两个主要挑战是图像先验的准确刻画与图像退化算子的精确建模。预训练扩散模型已在零样本图像复原方法中作为图像先验取得显著成功。然而，如何最优地处理退化算子仍是一个开放性问题。在真实世界数据中，依赖特定参数化退化模型假设的方法常面临适用性局限。为此，我们提出Invert2Restore——一种零样本、免训练的复原方法，可在完全盲与部分盲两种设置下运行：既无需退化模型的先验知识，也适用于仅知其参数化形式而参数未知的场景。尽管如此，Invert2Restore仍能实现高保真度的复原结果，并对各类图像退化具有良好泛化能力。该方法利用预训练扩散模型作为标准正态样本与无失真图像样本间的确定性映射。其核心洞见在于：扩散模型将退化图像映射至的输入噪声位于标准正态分布的低概率密度区域。因此，我们可以通过将输入噪声精细引导至高密度区域来实现退化图像的复原。我们通过多组图像复原任务对Invert2Restore进行实验验证，结果表明该方法在退化算子完全未知或部分已知的场景下均达到了最先进的性能水平。