Abstract
In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regression networks as continuous-time transport models. While pixel-wise I2I regression is simple, stable, and easy to adapt across tasks, it often over-smooths ill-posed and multimodal targets, whereas generative alternatives often require additional components, task-specific tuning, and more complex training and inference pipelines. Our method augments the backbone input by channel-wise concatenation with a noise-corrupted version of the ground-truth target and optimizes a simple t-reweighted pixel loss. This objective admits a rectified-flow interpretation via an induced velocity field, enabling ODE-based progressive refinement at inference time while largely preserving the standard supervised training pipeline. In most cases, adopting I2I-RFR requires only expanding the input channels, and inference can be performed with a few explicit solver steps (e.g., 3 steps) without distillation. Extensive experiments across multiple image-to-image translation and video restoration tasks show that I2I-RFR generally improves performance across a wide range of tasks and backbones, with particularly clear gains in perceptual quality and detail preservation. Overall, I2I-RFR provides a lightweight way to incorporate continuous-time refinement into conventional I2I models without requiring a heavy generative pipeline.
Abstract (translated)
在本工作中，我们提出了图像到图像整流流重构（I2I-RFR），这是一种实用的即插式重构方法，可将标准I2I回归网络重新诠释为连续时间传输模型。虽然逐像素I2I回归具有简单、稳定且易于跨任务适配的优点，但它常会过度平滑病态且多模态的目标，而生成式替代方案通常需要额外组件、任务特定调优以及更复杂的训练和推理流程。我们的方法通过将噪声扰动后的真实目标图像与主干网络输入进行通道级拼接来增强输入，并优化一个简单的t重加权像素损失。该目标函数可通过诱导的速度场获得整流流解释，从而在推理时实现基于ODE的渐进式细化，同时基本保持标准的监督训练流程。在大多数情况下，采用I2I-RFR仅需扩展输入通道数，且推理可通过少数显式求解器步骤（例如3步）完成，无需知识蒸馏。在多个图像到图像转换和视频恢复任务上的大量实验表明，I2I-RFR通常能提升广泛任务和主干网络的性能，尤其在感知质量和细节保留方面有显著提升。总体而言，I2I-RFR提供了一种轻量级方式，可将连续时间细化融入传统I2I模型，而无需构建复杂的生成式流程。
URL
https://arxiv.org/abs/2603.20186
