用于单图像反射去除的可逆解耦网络
Reversible Decoupling Network for Single Image Reflection Removal
AI总结：
针对单图像反射去除中高层语义信息易丢失及双流网络交互模式固定的问题，提出可逆解耦网络RDNet，利用可逆编码器灵活解耦透射与反射特征，并设计透射率感知提示生成器动态校准特征，在五个基准数据集及NTIRE 2025挑战赛中取得SOTA性能。
摘要：
近期基于深度学习的单图像反射去除方法展现出令人鼓舞的进展，主要有两个原因：1）利用识别预训练特征作为输入，以及2）双流交互网络的设计。然而，根据信息瓶颈原理，高层语义线索在逐层传播过程中往往会被压缩或丢弃。此外，双流网络中的交互在不同层间遵循固定模式，限制了整体性能。为解决这些局限，我们提出了一种名为可逆解耦网络（RDNet）的新架构，它采用可逆编码器来保留有价值的信息，同时在前向传播过程中灵活解耦透射相关和反射相关的特征。此外，我们定制了透射率感知提示生成器来动态校准特征，进一步提升性能。大量实验表明，RDNet在五个广泛采用的基准数据集上优于现有SOTA方法。RDNet在NTIRE 2025单图像反射去除野外挑战赛中，在保真度和感知比较方面均取得最佳性能。代码可在https://github.com/lime-j/RDNet获取。
英文摘要：
Recent deep-learning-based approaches to single-image reflection removal have shown promising advances, primarily for two reasons: 1) the utilization of recognition-pretrained features as inputs, and 2) the design of dual-stream interaction networks. However, according to the Information Bottleneck principle, high-level semantic clues tend to be compressed or discarded during layer-by-layer propagation. Additionally, interactions in dual-stream networks follow a fixed pattern across different layers, limiting overall performance. To address these limitations, we propose a novel architecture called Reversible Decoupling Network (RDNet), which employs a reversible encoder to secure valuable information while flexibly decoupling transmission- and reflection-relevant features during the forward pass. Furthermore, we customize a transmission-rate-aware prompt generator to dynamically calibrate features, further boosting performance. Extensive experiments demonstrate the superiority of RDNet over existing SOTA methods on five widely-adopted benchmark datasets. RDNet achieves the best performance in the NTIRE 2025 Single Image Reflection Removal in the Wild Challenge in both fidelity and perceptual comparison. Our code is available at https://github.com/lime-j/RDNet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
