Restora-Flow: Mask-Guided Image Restoration with Flow Matching

• URL: http://arxiv.org/abs/2511.20152v2
• Date: Wed, 26 Nov 2025 09:04:52 GMT
• Title: Restora-Flow: Mask-Guided Image Restoration with Flow Matching
• Authors: Arnela Hadzic, Franz Thaler, Lea Bogensperger, Simon Johannes Joham, Martin Urschler,
• Abstract summary: Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models.<n>We introduce Restora-Flow, a training-free method that guides flow matching sampling by a degradation mask.<n>We show superior perceptual quality and processing time compared to diffusion and flow matching-based reference methods.
• Score: 0.39141750421215127
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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