Reversible Decoupling Network for Single Image Reflection Removal
<|reference_start|>Reversible Decoupling Network for Single Image Reflection Removal: 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. Our code will be made publicly available.<|reference_end|>
@article{zhao2024reversible,
    title={Reversible Decoupling Network for Single Image Reflection Removal},
    author={Hao Zhao, Mingjia Li, Qiming Hu, Xiaojie Guo},
    journal={arXiv preprint arXiv:2410.08063},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.08063},
    primaryClass={cs.CV}
}