根据反射强度动态调整模型，提升单图去反射效果
ReLo-IRR: Reflection-Guided LoRA Framework for Image Reflection Removal
- 用轻量估计算法预测反射强度，指导模型自适应调节
- 结合时间步嵌入实现渐进式去反射，减少细节丢失
- 适合处理强弱反射并存的复杂场景，提升泛化能力
单图像反射去除（SIRR）旨在从含反射的图像中恢复干净的透射层。尽管近期基于大型扩散模型的方法取得良好效果，但其依赖图像无关的适配策略（如微调或ControlNet），对所有图像统一抑制反射，导致强反射残留、弱反射细节损失。为此，本文提出基于修正流模型的反射引导LoRA框架ReLo-IRR。首先设计轻量级估计算法，预测反射强度描述符，为每张图像提供显式的反射主导先验，实现图像相关的LoRA调制；其次引入时间条件机制，将反射描述符与时间步嵌入融合，使LoRA调制随粗到细的去噪过程动态演化。通过联合建模反射强度与去噪动态，ReLo-IRR在多种反射条件下均实现鲁棒抑制。大量实验在挑战性基准上验证了其有效性，表现出更优的去反射性能和鲁棒泛化能力。代码已开源：https://github.com/KONGBAI-8080/ReLo-IRR。
原文摘要 · Abstract (English)
Single-image reflection removal (SIRR) aims to recover the clean transmission layer from a reflection-contaminated image. Although recent methods achieve promising results with large diffusion models, they rely on image-agnostic adaptation strategies, e.g., fine-tuning or ControlNet, that enforce uniform suppression regardless of reflection severity. As a result, heavy reflections often leave residuals, while weak ones suffer from detail loss. To this end, we propose ReLo-IRR, a reflection-guided LoRA framework built upon the rectified flow model. First, a lightweight estimator is designed to predict the reflection strength descriptor, providing an explicit prior of reflection dominance for each image and enabling image-dependent LoRA modulation. Second, we introduce a time-conditioned mechanism that fuses this reflection descriptor with timestep embeddings, enabling LoRA modulation to evolve consistently with the coarse-to-fine denoising process. By jointly modeling reflection strength and denoising dynamics, our ReLo-IRR achieves robust suppression of diverse reflection conditions. Extensive experiments on challenging benchmarks validate the effectiveness of ReLo-IRR, demonstrating superior dereflection performance and robust generalization. The code is released at https://github.com/KONGBAI-8080/ReLo-IRR.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。