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.

翻译：图像反射分离旨在从混合图像中解耦出透射层与反射层。现有方法依赖单幅图像的有限信息，当两层对比度相近时易混淆二者，该挑战在夜间更为严峻。为解决此问题，我们提出深度记忆解耦网络（DMDNet）。该方法采用深度感知扫描（DAScan）引导Mamba聚焦显著结构，沿语义连贯性促进信息流以构建稳定状态。与DAScan协同工作的深度协同状态空间模型（DS-SSM）通过深度调制状态激活的敏感性，抑制干扰层解耦的模糊特征扩散。此外，我们引入记忆专家补偿模块（MECM），利用跨图像历史知识引导专家提供层特异性补偿。针对夜间反射分离数据集的缺失，我们构建了夜间图像反射分离（NightIRS）数据集。大量实验表明，DMDNet在日间与夜间场景下均优于现有最先进方法。