In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling and noise-residual coupling. To address this dilemma, we propose IR-Flow, a novel image restoration method based on Rectified Flow that serves as a unified framework bridging the gap between discriminative and generative paradigms. Specifically, we first construct multilevel data distribution flows, which expand the ability of models to learn from and adapt to various levels of degradation. Subsequently, cumulative velocity fields are proposed to learn transport trajectories across varying degradation levels, guiding intermediate states toward the clean target, while a multi-step consistency constraint is presented to enforce trajectory coherence and boost few-step restoration performance. We show that directly establishing a linear transport flow between degraded and clean image domains not only enables fast inference but also improves adaptability to out-of-distribution degradations. Extensive evaluations on deraining, denoising and raindrop removal tasks demonstrate that IR-Flow achieves competitive quantitative results with only a few sampling steps, offering an efficient and flexible framework that maintains an excellent distortion-perception balance. Our code is available at https://github.com/fanzh03/IR-Flow.

翻译：在图像修复中，单步判别式映射常因期望学习而缺失精细细节，而生成式范式则面临多步采样效率低下及噪声-残差耦合问题。为解决这一困境，我们提出IR-Flow——一种基于整流流的新型图像修复方法，作为统一框架弥合判别式与生成式范式之间的鸿沟。具体而言，我们首先构建多层级数据分布流，增强模型对不同退化程度的学习与适应能力。随后提出累积速度场，用于学习跨不同退化程度的传输轨迹，引导中间状态趋向干净目标，同时引入多步一致性约束以强化轨迹连贯性并提升少步修复性能。研究表明，直接在退化与干净图像域之间建立线性传输流，不仅能实现快速推理，还能增强对分布外退化的适应能力。在去雨、去噪及去除雨滴任务上的广泛评估表明，IR-Flow仅需少量采样步骤即可达到具有竞争力的量化结果，提供了在保持优异失真-感知平衡的同时兼具高效性与灵活性的框架。我们的代码开源于https://github.com/fanzh03/IR-Flow。