Restora-Flow：基于掩码的图像恢复与流匹配
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
AI总结：
Restora-Flow通过降质掩码引导流匹配采样并结合轨迹校正机制，实现高效且高质量的图像恢复。
摘要：
流匹配已成为一种有前景的生成方法，能够解决与最先进的扩散模型相关的采样时间长的问题，并使轨迹设计更加灵活，同时保持高质量的图像生成。这种能力使其成为图像恢复任务的生成先验。尽管当前利用流模型的方法在恢复任务中已显示出有希望的结果，但一些方法仍然存在处理时间长或生成过度平滑结果的问题。为了解决这些挑战，我们引入了Restora-Flow，这是一种无需训练的方法，通过降质掩码引导流匹配采样，并结合轨迹校正机制以确保与降质输入的一致性。我们在自然和医学数据集上评估了我们的方法，涵盖多种涉及基于掩码的降质的图像恢复任务，即修复、超分辨率和去噪。我们展示了与基于扩散和流匹配的参考方法相比，具有更优的感知质量和处理时间。
英文摘要：
Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible trajectory design, while maintaining high-quality image generation. This capability makes it suitable as a generative prior for image restoration tasks. Although current methods leveraging flow models have shown promising results in restoration, some still suffer from long processing times or produce over-smoothed results. To address these challenges, we introduce Restora-Flow, a training-free method that guides flow matching sampling by a degradation mask and incorporates a trajectory correction mechanism to enforce consistency with degraded inputs. We evaluate our approach on both natural and medical datasets across several image restoration tasks involving a mask-based degradation, i.e., inpainting, super-resolution and denoising. We show superior perceptual quality and processing time compared to diffusion and flow matching-based reference methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
