DeshadowMamba：将图像去阴影视为1D序列相似性问题
DeshadowMamba: Deshadowing as 1D Sequential Similarity
AI总结：
针对现有去阴影模型结构扭曲、颜色不一致的问题，提出结合CrossGate定向调制与ColorShift正则化的DeshadowMamba模型，适配去阴影需求，在公开基准上实现SOTA性能。
摘要：
当前用于图像去阴影的深度模型通常依赖基于注意力的架构来捕获长距离依赖关系，但这类模型固定的注意力模式容易混合无关区域的光照线索，导致结构扭曲和颜色不一致。本研究从序列建模的视角重新审视去阴影任务，探索应用Mamba——一种通过定向状态转移传播全局上下文的选择性状态空间模型，这类转移既能实现高效的全局感受野，又能保留位置连续性。尽管Mamba具备潜力，但直接将其应用于图像数据效果不佳，因为它缺乏阴影-非阴影语义感知，且易受邻近区域的颜色干扰。为解决这些局限，我们提出CrossGate，这是一种定向调制机制，将阴影感知相似性注入Mamba的输入门，实现沿转移轴对相关上下文的选择性整合。为进一步保证外观保真度，我们引入ColorShift正则化，这是一种由全局颜色统计驱动的对比学习目标，通过合成结构化的信息性负样本，引导模型抑制颜色污染，实现鲁棒的颜色恢复。这些组件共同让序列建模适配去阴影任务所需的结构完整性和色彩一致性。在公开基准上的大量实验表明，DeshadowMamba达到了当前最优的视觉质量和出色的量化性能。
英文摘要：
Recent deep models for image shadow removal often rely on attention-based architectures to capture long-range dependencies. However, their fixed attention patterns tend to mix illumination cues from irrelevant regions, leading to distorted structures and inconsistent colors. In this work, we revisit shadow removal from a sequence modeling perspective and explore the use of Mamba, a selective state space model that propagates global context through directional state transitions. These transitions yield an efficient global receptive field while preserving positional continuity. Despite its potential, directly applying Mamba to image data is suboptimal, since it lacks awareness of shadow-non-shadow semantics and remains susceptible to color interference from nearby regions. To address these limitations, we propose CrossGate, a directional modulation mechanism that injects shadow-aware similarity into Mamba's input gate, allowing selective integration of relevant context along transition axes. To further ensure appearance fidelity, we introduce ColorShift regularization, a contrastive learning objective driven by global color statistics. By synthesizing structured informative negatives, it guides the model to suppress color contamination and achieve robust color restoration. Together, these components adapt sequence modeling to the structural integrity and chromatic consistency required for shadow removal. Extensive experiments on public benchmarks demonstrate that DeshadowMamba achieves state-of-the-art visual quality and strong quantitative performance.
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