Improving Image-to-Image Translation via a Rectified Flow Reformulation

In this work, we propose Image-to-Image Rectified Flow Re-formulation (I2I-RFR), a practical plug-in reformulation that recastsstandard I2I regression networks as continuous-time transport models.While pixel-wise I2I regression is simple, stable, and easy to adapt acrosstasks, it often over-smooths ill-posed and multi-modal targets, whereasgenerative alternatives often require additional components, task-specifictuning, and more complex training and inference pipelines. Our methodaugments the backbone input by channel-wise concatenation with a noise-corrupted version of the ground-truth target and optimizes a simplet-reweighted pixel loss. This objective admits a rectified-flow interpre-tation via an induced velocity field, enabling ODE-based progressiverefinement at inference time while largely preserving the standard su-pervised training pipeline. In most cases, adopting I2I-RFR requiresonly expanding the input channels, and inference can be performed witha few explicit solver steps (e.g., 3 steps) without distillation. Exten-sive experiments across multiple image-to-image translation and videorestoration tasks show broad perceptual improvements, with possibleperception–distortion trade-offs in highly pixel-aligned settings such assuper-resolution.