The reflection superposition phenomenon is complex and widely distributed in the real world, which derives various simplified linear and nonlinear formulations of the problem. In this paper, based on the investigation of the weaknesses of existing models, we propose a more general form of the superposition model by introducing a learnable residue term, which can effectively capture residual information during decomposition, guiding the separated layers to be complete. In order to fully capitalize on its advantages, we further design the network structure elaborately, including a novel dual-stream interaction mechanism and a powerful decomposition network with a semantic pyramid encoder. Extensive experiments and ablation studies are conducted to verify our superiority over state-of-the-art approaches on multiple real-world benchmark datasets. Our code is publicly available at https://github.com/mingcv/DSRNet.

基于对现有模型弱点的调查，我们提出了一种更一般的叠加模型，引入可学习的残差项，可以有效地捕捉分解过程中的剩余信息，引导分离层的完整性。为了充分利用其优势，我们还精心设计了网络结构，包括新颖的双流交互机制和具有语义金字塔编码器的强大分解网络。通过大量实验证明，在多个真实世界基准数据集上，我们的方法优于现有技术。我们的代码公开可用于此https网址。

基于组件协同的单幅图像反射分离