基于内核部分最小平方的色彩成像系统的色度表征
Siyu Zhao1, Lu Liu1, Zibing Feng1
1School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种新的内核部分最小平方 (KPLS) 方法,用于在成像系统中准确的色度表征. 该KPLS模型表现出优越的性能比现有方法,增强色彩信息管理.
科学领域:
- 颜色科学 颜色科学
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 准确的色度表征对于数字成像中的色彩信息管理至关重要.
- 现有的方法,如非线性回归和神经网络在精度上有局限性.
研究的目的:
- 提出并验证使用内核部分最小平方 (KPLS) 的新型色度表征方法.
- 为了提高成像系统中颜色空间转换的预测准确度.
主要方法:
- 使用了核心函数扩展RGB响应值作为输入特征.
- 用CIE-1931 XYZ作为KPLS模型的输出向量.
- 通过嵌套交叉验证和网格搜索确定超参数.
主要成果:
- 与加权非线性回归和神经网络模型相比,KPLS模型实现了更高的性能.
- 使用ColorChecker SG图表进行的实验验证显示,预测准确度很高.
- 使用CIELAB,CIELUV和CIEDE2000颜色差异指标进行评估.
结论:
- 拟议的KPLS方法为色度表征提供了一种强大而准确的方法.
- 这种方法显著提高了彩色成像系统中的彩色信息管理.
- 该KPLS模型提供了可靠的色彩空间转换与良好的预测准确性.
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