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.
@article{arxiv.2511.20152,
title = {Restora-Flow: Mask-Guided Image Restoration with Flow Matching},
author = {Arnela Hadzic and Franz Thaler and Lea Bogensperger and Simon Johannes Joham and Martin Urschler},
journal= {arXiv preprint arXiv:2511.20152},
year = {2025}
}