提出轻量级区域注意力机制，提升去阴影精度与效率
Regional Attention for Shadow Removal
- 设计区域注意力机制，聚焦阴影与非阴影区域的上下文关联
- 模型参数量更少，计算开销降低，仍保持高精度去阴影效果
- 适合实际应用，尤其对资源受限场景友好
阴影是光与物体相互作用的自然结果，在塑造图像美学方面起关键作用，但也会影响内容可见性和整体视觉质量。近年来的去阴影方法常采用注意力机制，因其有效性而成为核心组件，但普遍存在模型过大、计算复杂度高的问题。为此，本文提出一种轻量且精确的去阴影框架。首先分析去阴影任务特性，挖掘重建阴影区域所需的关键信息，设计新型区域注意力机制以有效捕捉此类信息；随后构建区域注意力去阴影模型（RASM），利用非阴影区域辅助恢复阴影区域。与现有注意力模型不同，本方法使每个阴影区域能更合理地与其邻近非阴影区域交互，充分挖掘阴影与非阴影区域间的局部上下文相关性。大量实验表明，所提方法在准确率与效率上均优于当前主流模型，具备良好实用性。
原文摘要 · Abstract (English)
Shadow, as a natural consequence of light interacting with objects, plays a crucial role in shaping the aesthetics of an image, which however also impairs the content visibility and overall visual quality. Recent shadow removal approaches employ the mechanism of attention, due to its effectiveness, as a key component. However, they often suffer from two issues including large model size and high computational complexity for practical use. To address these shortcomings, this work devises a lightweight yet accurate shadow removal framework. First, we analyze the characteristics of the shadow removal task to seek the key information required for reconstructing shadow regions and designing a novel regional attention mechanism to effectively capture such information. Then, we customize a Regional Attention Shadow Removal Model (RASM, in short), which leverages non-shadow areas to assist in restoring shadow ones. Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas, for seeking the regional contextual correlation between shadow and non-shadow areas. Extensive experiments are conducted to demonstrate that our proposed method delivers superior performance over other state-of-the-art models in terms of accuracy and efficiency, making it appealing for practical applications.
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