Reflection removal of a single image remains a highly challenging task due to the complex entanglement between target scenes and unwanted reflections. Despite significant progress, existing methods are hindered by the scarcity of high-quality, diverse data and insufficient restoration priors, resulting in limited generalization across various real-world scenarios. In this paper, we propose Dereflection Any Image, a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal. First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes, enabling variation of reflection angles and intensities, and setting a new benchmark in scale, quality, and diversity. Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference. To ensure stable learning, we design a three-stage progressive training strategy, including reflection-invariant finetuning to encourage consistent outputs across varying reflection patterns that characterize our dataset. Extensive experiments show that our method achieves SOTA performance on both common benchmarks and challenging in-the-wild images, showing superior generalization across diverse real-world scenes.

翻译：单幅图像的去反射任务由于目标场景与干扰反射之间的复杂纠缠关系，仍是一项极具挑战性的课题。尽管已取得显著进展，现有方法受限于高质量多样化数据的稀缺性以及复原先验的不足，导致其在各类真实场景中的泛化能力有限。本文提出“任意图像去反射”方法——一种包含高效数据准备流程与泛化性模型的鲁棒去反射综合解决方案。首先，我们通过随机旋转目标场景中的反射介质，构建了名为“多样化去反射数据集”的新数据集，该数据集能够实现反射角度与强度的多样化调节，并在规模、质量与多样性方面设立了新的基准。其次，我们提出一种基于扩散模型的框架，采用单步扩散实现确定性输出与快速推理。为确保稳定学习，我们设计了包含三阶段的渐进式训练策略，其中反射不变性微调旨在促使模型对数据集中表征的不同反射模式产生一致输出。大量实验表明，我们的方法在常规基准测试与极具挑战性的真实场景图像上均达到最先进性能，展现出对多样化真实场景的卓越泛化能力。