Reversible Decoupling Network for Single Image Reflection Removal

• URL: http://arxiv.org/abs/2410.08063v1
• Date: Thu, 10 Oct 2024 15:58:27 GMT
• Title: Reversible Decoupling Network for Single Image Reflection Removal
• Authors: Hao Zhao, Mingjia Li, Qiming Hu, Xiaojie Guo,
• Abstract summary: High-level semantic clues tend to be compressed or discarded during layer-by-layer propagation. We propose a novel architecture called Reversible Decoupling Network (RDNet) RDNet employs a reversible encoder to secure valuable information while flexibly decoupling transmission- and reflection-relevant features during the forward pass.
• Score: 15.763420129991255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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