Diffusion models have become a successful approach for solving various image inverse problems by providing a powerful diffusion prior. Many studies tried to combine the measurement into diffusion by score function replacement, matrix decomposition, or optimization algorithms, but it is hard to balance the data consistency and realness. The slow sampling speed is also a main obstacle to its wide application. To address the challenges, we propose Deep Data Consistency (DDC) to update the data consistency step with a deep learning model when solving inverse problems with diffusion models. By analyzing existing methods, the variational bound training objective is used to maximize the conditional posterior and reduce its impact on the diffusion process. In comparison with state-of-the-art methods in linear and non-linear tasks, DDC demonstrates its outstanding performance of both similarity and realness metrics in generating high-quality solutions with only 5 inference steps in 0.77 seconds on average. In addition, the robustness of DDC is well illustrated in the experiments across datasets, with large noise and the capacity to solve multiple tasks in only one pre-trained model.

翻译：扩散模型通过提供强大的扩散先验，已成为解决各类图像逆问题的成功方法。许多研究尝试通过评分函数替换、矩阵分解或优化算法将测量信息融入扩散过程，但难以在数据一致性与真实性之间取得平衡。采样速度慢也是制约其广泛应用的主要障碍。为解决这些挑战，我们提出深度数据一致性（DDC），在利用扩散模型求解逆问题时，通过深度学习模型更新数据一致性步骤。通过分析现有方法，采用变分界训练目标最大化条件后验概率，并降低其对扩散过程的影响。与线性及非线性任务中的最先进方法相比，DDC在生成高质量解时展现出相似度与真实性指标的卓越性能，仅需5次推理步骤，平均耗时0.77秒。此外，DDC的鲁棒性在跨数据集实验、大噪声场景及单一预训练模型解决多任务的能力中得到了充分验证。