Abstract
In this paper, we introduce Plug-and-Play (PnP) Flow Matching, an algorithm for solving imaging inverse problems. PnP methods leverage the strength of pre-trained denoisers, often deep neural networks, by integrating them in optimization schemes. While they achieve state-of-the-art performance on various inverse problems in imaging, PnP approaches face inherent limitations on more generative tasks like inpainting. On the other hand, generative models such as Flow Matching pushed the boundary in image sampling yet lack a clear method for efficient use in image restoration. We propose to combine the PnP framework with Flow Matching (FM) by defining a time-dependent denoiser using a pre-trained FM model. Our algorithm alternates between gradient descent steps on the data-fidelity term, reprojections onto the learned FM path, and denoising. Notably, our method is computationally efficient and memory-friendly, as it avoids backpropagation through ODEs and trace computations. We evaluate its performance on denoising, super-resolution, deblurring, and inpainting tasks, demonstrating superior results compared to existing PnP algorithms and Flow Matching based state-of-the-art methods.
Abstract (translated)
在本文中,我们提出了Plug-and-Play(PnP)流匹配算法,用于解决图像反问题。PnP方法通过将预训练的去噪器集成到优化方案中,利用预训练去噪器的优势,通常使用深度神经网络。虽然它们在各种图像反问题中实现了最先进的性能,但PnP方法在更具有生成性的任务(如修复)上存在固有局限性。另一方面,像Flow Matching这样的生成模型在图像采样方面推动了边界,但是它们缺乏在图像修复中有效使用的方法。我们提出了一种将PnP框架与Flow Matching(FM)相结合的方法,通过使用预训练FM模型定义一个时间依赖的去噪器。我们的算法在数据可靠性梯度下降步骤、学习到的FM路径上的投影以及去噪三个步骤之间交替进行。值得注意的是,我们的方法在计算效率和内存友好性方面具有优势,因为它避免了通过ODE进行反向传播和迹计算。我们在去噪、超分辨率、去雾和修复任务上评估了其性能,证明了与现有PnP算法和基于Flow Matching的最佳方法相比具有卓越的结果。
URL
https://arxiv.org/abs/2410.02423