获取物体的光谱反射率是准确再现物体在各种光照条件下真实颜色的关键保证, 这对纺织服装、 出版印刷、 网络电商、 远程医疗等对颜色有较高要求的行业有重要作用。 光谱反射率重建的目的是利用训练样本建立数码相机等通用设备所获取的RGB三色值和光谱反射率高维向量间的映射关系, 从而避免使用分光光度计等专业设备所带来的成本高、 操作复杂、 分辨率低等问题。 训练样本的选择是影响光谱反射率重建算法效果的重要因素。 从物理角度看, 光谱反射率是一条关于波长的光滑曲线, 光谱反射率向量最大的相关性特征就是其光滑性, 因此, 训练样本的选择应同时考虑空间距离和形状的相似性。 针对局部学习方法中局部样本选择问题, 提出一种能同时考虑光谱反射率向量形状相似和空间距离相近的更加有效的训练样本选取方法, 以提高光谱反射率重建的精度。 该方法利用待测样本与训练样本之间的加权欧氏距离与向量夹角距离结合后赋予不同权重作为相似性度量, 根据样本容量动态地选出相似度较高的样本。 实验以孟赛尔半光泽数据集(munsell matte)为样本集, 基于伪逆法进行光谱反射率重建, 以光谱均方根误差和色差为评价指标, 与加权欧氏距离方法从样本选择的有效性和重构精度两方面进行比较。 实验结果表明, 基于改进加权欧氏距离的样本选择, 能够在保证均方误差最小的条件下, 显著降低色度误差, 同时添加不同噪声水平后, 文中方法的均方根误差和平均色差依旧保持最小, 该方法能够更好地利用局部样本的信息, 而且具有较好的抗干扰能力, 可以有效地提高光谱反射率重建的实际应用效果, 进而为颜色的真实再现提供保障。

Obtaining the spectral reflectance of an object is the key to accurately reproducing an objects true color under various lighting conditions, which plays an important role in industries with high color requirements, such as textiles and clothing, publishing and printing, online e-commerce, telemedicine, etc. The purpose of spectral reflectance reconstruction is to use training samples to establish the mapping relationship between RGB trichromatic values and high-dimensional vector of spectral reflectance obtained by general equipment such as digital cameras to avoid the problems of high cost, complex operation and low resolution caused by the use of a spectrophotometer and other professional equipment. Due to the limitation of uneven or inconsistent training sample distribution, the selection of training sample sets greatly impacts the spectral reflectance reconstruction processes. The representative color samples selection for local learning-based spectral reflectance reconstruction are discussed in this paper. From a physical point of view, the spectral reflectance vector is a smooth curve, and the selection of training samples should consider both the spatial distance and the similarity of the shape. A method based on improved weighted Euclidean distance is proposed for sample selection. The weighted Euclidean distance between the testing sample and the training sample is combined with the vector angle distance, and different weights are given as the similarity measure, which aims to ensure the similarity between training samples and target samples. The experimental results show that the proposed method can significantly reduce the chromaticity error while ensuring the minimum root mean square error. Moreover, after adding noise, it maintains the minimum root mean square error and chromaticity error, showing the method has good generalization performance.