Single Image Reflection Separation via Component Synergy
Qiming Hu Xiaojie Guo
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.