论文标题
JPEG伪影使用denoising扩散恢复模型进行校正
JPEG Artifact Correction using Denoising Diffusion Restoration Models
论文作者
论文摘要
扩散模型可以用作解决各种反问题的学习先验。但是，大多数现有方法仅限于线性问题，将其适用性限制在更普遍的情况下。在本文中，我们建立在deno的扩散恢复模型（DDRM）的基础上，并提出了一种解决某些非线性反问题的方法。我们利用DDRM中使用的伪内运算符，并将此概念推广到其他测量操作员，这使我们能够使用预先训练的无条件扩散模型进行JPEG伪影校正等应用。我们从经验上证明了我们方法在各种质量因素上的有效性，从而达到了专门针对JPEG恢复任务培训的最先进方法的性能水平。
Diffusion models can be used as learned priors for solving various inverse problems. However, most existing approaches are restricted to linear inverse problems, limiting their applicability to more general cases. In this paper, we build upon Denoising Diffusion Restoration Models (DDRM) and propose a method for solving some non-linear inverse problems. We leverage the pseudo-inverse operator used in DDRM and generalize this concept for other measurement operators, which allows us to use pre-trained unconditional diffusion models for applications such as JPEG artifact correction. We empirically demonstrate the effectiveness of our approach across various quality factors, attaining performance levels that are on par with state-of-the-art methods trained specifically for the JPEG restoration task.
