论文标题
使用歧管约束改善反问题的扩散模型
Improving Diffusion Models for Inverse Problems using Manifold Constraints
论文作者
论文摘要
最近，扩散模型已用于以无监督的方式解决各种反问题，并对采样过程进行了适当的修改。但是，当前的求解器递归地应用反向扩散步骤，然后进行基于投影的测量一致性步骤，通常会产生次优的结果。通过研究生成抽样路径，我们在这里表明当前的求解器将样本路径从数据歧管上丢弃，因此误差会累积。为了解决这个问题，我们提出了一个受歧管约束启发的额外更正项，该术语可以与先前的求解器协同使用，以使迭代接近歧管。所提出的歧管约束很容易在几行代码内实施，但可以提高性能的惊人幅度。通过广泛的实验，我们表明我们的方法在理论上和经验上都优于先前的方法，在许多应用中产生了令人鼓舞的结果，例如图像入入，着色和稀疏视图计算机断层扫描。代码可用https://github.com/hj-harry/mcg_diffusion
Recently, diffusion models have been used to solve various inverse problems in an unsupervised manner with appropriate modifications to the sampling process. However, the current solvers, which recursively apply a reverse diffusion step followed by a projection-based measurement consistency step, often produce suboptimal results. By studying the generative sampling path, here we show that current solvers throw the sample path off the data manifold, and hence the error accumulates. To address this, we propose an additional correction term inspired by the manifold constraint, which can be used synergistically with the previous solvers to make the iterations close to the manifold. The proposed manifold constraint is straightforward to implement within a few lines of code, yet boosts the performance by a surprisingly large margin. With extensive experiments, we show that our method is superior to the previous methods both theoretically and empirically, producing promising results in many applications such as image inpainting, colorization, and sparse-view computed tomography. Code available https://github.com/HJ-harry/MCG_diffusion
