一个影子成像双线模型和三分支残余网络用于影子去除
IEEE transactions on neural networks and learning systems
|August 2, 2023
概括
本研究引入了一种新型的三分支残余 (TBR) 网络,用于高效地去除单图像影子. TBR 网络简化了管道,提高了影子清除精度和文物减少.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 目前的影子去除方法经常使用影子面具,这些面具与半影和小影子作斗争,导致复杂的管道.
- 现有的技术只能增加阴影亮度,可以引入文物.
研究的目的:
- 开发一种更有效,更准确的单图片影子去除方法.
- 解决现有的影子去除管道的局限性,包括工件生成和处理复杂的影子类型.
主要方法:
- 提出了一种影子成像双线模型,以了解影子去除过程.
- 设计了一种新的三分支残余 (TBR) 网络,用于删除阴影.
- 开发了一个单阶段网络,集成照明补偿,影子重建,影子色估计和影子移除.
主要成果:
- 通过TBR网络,消除了单独检测和精炼网络的需要,从而显著缩短了影子清除管道.
- 拟议的方法有效地恢复阴影区域的光强度,同时保留非阴影区域.
- 实验结果显示,与最先进的影子清除技术相比,其性能优越.
结论:
- 新的影子成像双线模型提供了对影子移除复杂性的洞察.
- 开发的TBR网络提供了一种高效和有效的解决方案,用于单个图像的影子去除,优于现有的方法.
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