Single Image-Based Reflection Removal via Dual-Stream Multi-Column Reversible Encoding

Reflection removal from a single image is an ill-posed problem due to the inherent ambiguity in separating transmission and reflection components from a single composite observation. In this paper, we address this challenge by introducing a reversible feature encoding strategy combined with a simplified dual-stream decoding structure. In particular, the reversible NAFNet encoder enables us to retain all feature information throughout the encoding process while avoiding memory overhead, an aspect that is crucial for separating overlapping structures. In place of complex gated mechanisms, the proposed dual-stream decoder leverages shared encoder features and skip connections, thus enabling implicit bidirectional information flow between transmission and reflection streams. Although our model adopts a lightweight structure and omits attention modules, it achieves competitive results on standard reflection removal benchmarks, indicating that efficient and interpretable designs can match or surpass more complex counterparts.