Single image reflection separation aims to separate the transmission and reflection layers from a mixed image. Existing methods typically combine general priors from pre-trained models with task-specific priors such as text prompts and reflection detection. However, the transmission prior, as the most direct task-specific prior for the target transmission layer, has not been effectively modeled or fully utilized, limiting performance in complex scenarios. To address this issue, we propose a dual-prior interaction framework based on lightweight transmission prior generation and effective prior fusion. First, we design a Local Linear Correction Network (LLCN) that finetunes pre-trained models based on the physical constraint T=SI+B, where S and B represent pixel-wise and channel-wise scaling and bias transformations. LLCN efficiently generates high-quality transmission priors with minimal parameters. Second, we construct a Dual-Prior Interaction Transformer (DPIT) that employs a dual-stream channel reorganization attention mechanism. By reorganizing features from general and transmission priors for attention computation, DPIT achieves deep fusion of both priors, fully exploiting their complementary information. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.

翻译：单图像反射分离旨在从混合图像中分离出透射层与反射层。现有方法通常将预训练模型的通用先验与文本提示、反射检测等任务特定先验相结合。然而，作为目标透射层最直接的任务特定先验，透射先验尚未得到有效建模或充分利用，限制了方法在复杂场景下的性能。为解决该问题，我们提出一种基于轻量化透射先验生成与高效先验融合的双重先验交互框架。首先，我们设计局部线性校正网络（LLCN），该网络基于物理约束T=SI+B（其中S与B分别表示逐像素与逐通道的缩放偏置变换）对预训练模型进行微调。LLCN能以极少参数量高效生成高质量透射先验。其次，我们构建双重先验交互Transformer（DPIT），采用双流通道重组注意力机制。通过重组通用先验与透射先验的特征进行注意力计算，DPIT实现了双重先验的深度融合，充分挖掘其互补信息。在多个基准数据集上的实验结果表明，所提方法取得了最先进的性能。