多光谱成像是一项非常有前景的图像高保真获取与再现技术, 近年来在水下物体颜色还原的应用中也受到的极大的需求和关注。然而, 不同于空气中的物体的成像过程, 在水下成像过程中, 当光通过水而进行传播, 光被水体严重吸收和散射, 导致图像变暗, 在其光谱和颜色方面发生模糊和扭曲。文中讨论的是基于水下图像的水衰减系数的校准和其多光谱图像的光谱重构。首先在不同的距离处获取物体的图像, 提出了基于不同距离的图像进行水体衰减系数的校准并恢复原始图像的技术; 在此基础上, 分析并导出满足系数校准和图像复原所需的在不同距离获取到的最少的原始图像个数。最后, 通过比较复原的水下图像与空气中获取的彩色图像, 实验结果证明: 文中提出的技术能够对水下光谱图像的进行精确颜色复原, 所有测试图像的平均相对残留误差仅为5.87％。

A method based on the XYZLMS interim connection space is proposed to accurately acquire the multi-spectral images by digital still cameras. The XYZLMS values are firstly predicted from RGB values by polynomial model with local training samples and then spectral reflectance is constructed from XYZLMS values by pseudo-inverse method. An experiment is implemented for multi-spectral image acquisition based on a commercial digital still camera. The results indicate that multi-spectral images can be accurately acquired except the very dark colors.

To improve the color reproduction accuracy of image among digital devices in spectral color reproduction, a novel profile connection space is proposed. The profile connection space is composed of three sets of tristimulus determined by the principal components of typical illuminants and CIE1931XYZ standard colorimetric observer. The spectral reflectance of three common used atlases is transformed to three deformations of the profile connection space, and then transformed back to the spectral reflectance. The color difference between the original and transformed reflectance are very small for the common used illuminants, which indicates that the profile connection space is a good candidate for spectral color reproduction.

由于液晶显示器不能完全满足通道独立性和色品恒定性而导致其色度特征化精度不高，故液晶显示器精确色度特征化模型的研究具有重要意义。将分空间处理的思想应用于对液晶显示器色度特征化精度较高的掩模（Masking）模型，提出了分空间补偿（SC）模型。该模型将RGB颜色空间分为8个子空间，基于不同子空间的相应训练样本，将掩模模型的预测三刺激值差值拟合成各通道驱动值的多项式函数，并对其三刺激值进行补偿。通过对四台液晶显示器色度特征化的实验测试表明，分空间补偿模型的最优平均预测色差分别为0.2783，0.3199，0.7090和1.2216 CIELAB单位，验证了其颜色预测精度优于现有的相关色度特征化模型，且可以适用于各类液晶显示器。

为了提高数字图像设备的色度特征化精度，提出了基于高斯函数的加权自适应多项式模型。对任一目标样本以自适应方式选取距其最近的几个训练样本，设计相应高斯函数作为权重对各个训练样本的贡献率加以调制，通过最小二乘法获得目标样本对应的多项式系数。设计相应实验分别用该模型、普通多项式模型和基于距离反比函数的加权多项式模型实现了数码相机的色度特征化。实验结果表明，在所有训练样本和检验样本组合情况下，提出的基于高斯函数的加权自适应多项式模型均取得了最高的颜色预测精度。

To improve the accuracy of color image reproduction from displays to printers, an adaptively spatial color gamut mapping algorithm (ASCGMA) is proposed. In this algorithm, the compression degree of out-of-reproduction-gamut color is not only related to the position of the color in CIELCH color space, but also depending on the neighborhood of the color to be mapped. The psychophysical experiment of pair comparison is carried out to evaluate and compare this new algorithm with the HPMINDE and SGCK gamut mapping algorithms recommended by the International Commission on Illumination (CIE). The experimental results indicate that the proposed algorithm outperforms the algorithms of HPMINDE and SGCK except for the very dark images.

由数码相机信号直接重构物体表面的光谱信息对不同照明条件下颜色的复现十分重要。基于普通商用数码相机分别摄取两种CIE标准光源D65和A下颜色样品的RGB信号, 采用主元分析法(PCA)和多项式模型的结合算法进行物体表面色光谱反射比的重构研究。实验结果表明, 同时应用两种光源下的RGB信号可有效提高光谱重构精度, 并能较好地再现色样的同色异谱特性。