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黄颖, 雷秀杰, 姜茂, 李豪, 房少杰, 钱鹰. 基于特征分离和差分映射的阴影去除算法[J]. 计算机辅助设计与图形学学报. DOI: 10.3724/SP.J.1089.null.2023-00557
引用本文: 黄颖, 雷秀杰, 姜茂, 李豪, 房少杰, 钱鹰. 基于特征分离和差分映射的阴影去除算法[J]. 计算机辅助设计与图形学学报. DOI: 10.3724/SP.J.1089.null.2023-00557
Ying Huang, Xiujie Lei, Jiang Mao, Hao Li, Shaojie Fang, Ying Qian. Feature separation and differential mapping based shadow removal algorithm[J]. Journal of Computer-Aided Design & Computer Graphics. DOI: 10.3724/SP.J.1089.null.2023-00557
Citation: Ying Huang, Xiujie Lei, Jiang Mao, Hao Li, Shaojie Fang, Ying Qian. Feature separation and differential mapping based shadow removal algorithm[J]. Journal of Computer-Aided Design & Computer Graphics. DOI: 10.3724/SP.J.1089.null.2023-00557

基于特征分离和差分映射的阴影去除算法

Feature separation and differential mapping based shadow removal algorithm

  • 摘要: 阴影使得很多计算机视觉任务表现不佳,其与场景的复杂形成关系使其成为研究热点。之前的研究大致可分为两类:1)通过人工掩膜得到阴影区域,学习阴影的特征并应用到阴影去除;2)学习从阴影图像到无阴影图像的直接映射。第1)类方法人工标注成本较高;第2)类方法可能会对本不应参与映射的非阴影区域产生干扰。因此本文提出了分离重建网络(SRNet),一种借鉴独立成分分析的思想将阴影特征与环境特征分离,并对分离后的特征进行差异性映射从而实现的阴影去除方法。本方法可以将主要映射作用于阴影相关特征,而对非阴影区域产生较小的干扰,从而实现更好的阴影去除效果。仿真结果表明,本文所提出阴影去除算法相较于当前众多先进方法取得了较有竞争力的结果。

     

    Abstract: Shadow makes many computer vision tasks perform poorly, and the complex relationship between the shadow and scene makes it a research hotspot. Previous studies can be divided into two categories: 1) obtain shadow information by manual mask and remove it; 2) learn a direct mapping from shadow image to shadow-free image. The 1) method has a higher cost of manual annotation; the 2) method may interfere with non-shadow regions that should not be mapped. Therefore, the Separation and Reconstruction Network was proposed, a shadow removal method that uses an Independent Component Analysis-like method to separate the shadow features and the scene features from shadow images and realize shadow removal by differential mapping. This method focuses the primary mapping on shadow-related features while causing minimal interference in non-shadow areas, resulting in better shadow removal effects. Simulation results demonstrate that this paper's proposed shadow removal algorithm achieves competitive results compared to numerous state-of-the-art methods.

     

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