通过深度与记忆协同机制，提升全天候图像反射分离效果。
Depth-Synergized Mamba Meets Memory Experts for All-Day Image Reflection Separation
- 结合深度感知扫描与状态空间模型，增强语义连贯性信息流。
- 在夜间数据上实现优于现有方法的分离精度，夜间性能提升显著。
- 适合需要高精度反射分离的夜视成像、安防监控场景。
图像反射分离旨在从混合图像中分离出透射层和反射层。现有方法仅依赖单幅图像的有限信息，在两层对比度相似时容易混淆，夜间问题尤为严重。为此，我们提出深度-记忆解耦网络（DMDNet）。该网络采用深度感知扫描（DAScan）引导Mamba关注显著结构，促进沿语义一致性的信息传递，构建稳定状态。配合DAScan，深度协同状态空间模型（DS-SSM）通过深度调节状态激活敏感度，抑制干扰层解耦的模糊特征传播。此外，引入记忆专家补偿模块（MECM），利用跨图像历史知识指导专家进行层特定补偿。为弥补夜间反射分离数据集不足，我们构建了夜间图像反射分离（NightIRS）数据集。大量实验表明，DMDNet在昼夜条件下均优于当前最优方法。
原文摘要 · Abstract (English)
Image reflection separation aims to disentangle the transmission layer and the reflection layer from a blended image. Existing methods rely on limited information from a single image, tending to confuse the two layers when their contrasts are similar, a challenge more severe at night. To address this issue, we propose the Depth-Memory Decoupling Network (DMDNet). It employs the Depth-Aware Scanning (DAScan) to guide Mamba toward salient structures, promoting information flow along semantic coherence to construct stable states. Working in synergy with DAScan, the Depth-Synergized State-Space Model (DS-SSM) modulates the sensitivity of state activations by depth, suppressing the spread of ambiguous features that interfere with layer disentanglement. Furthermore, we introduce the Memory Expert Compensation Module (MECM), leveraging cross-image historical knowledge to guide experts in providing layer-specific compensation. To address the lack of datasets for nighttime reflection separation, we construct the Nighttime Image Reflection Separation (NightIRS) dataset. Extensive experiments demonstrate that DMDNet outperforms state-of-the-art methods in both daytime and nighttime.
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