文章摘要
李杰,王海文,王永伟,陈广学.面向高保真再现的多光谱图像降维方法[J].包装工程,2016,37(11):176-180.
LI Jie,WANG Hai-wen,WANG Yong-wei,CHEN Guang-xue.Multispectral Image Dimensionality Deduction Method for High-fidelity Reproduction[J].Packaging Engineering,2016,37(11):176-180.
面向高保真再现的多光谱图像降维方法
Multispectral Image Dimensionality Deduction Method for High-fidelity Reproduction
投稿时间:2015-11-11  修订日期:2016-06-10
DOI:
中文关键词: 多光谱图像降维  高保真再现  多光谱颜色复制  离散小波变换  非负主成分分析法
英文关键词: multispectral image dimensionality deduction  high-fidelity reproduction  multispectral color reproduction  discrete wavelet transform  nonnegative principal component analysis
基金项目:国家自然科学基金 (60972134);浙江省公益技术应用研究计划(2016C31080);浙江省重点技术创新专项计划(2015-422);浙江省衢州市科技计划指导性项目(2015019);衢州职业技术学院科研项目(QZYY1516);衢州职业技术学院内部培育项目(XXGC1403)
作者单位
李杰 衢州职业技术学院衢州 324000 
王海文 1.浙江科技学院杭州 310023 2.浙江美浓世纪集团杭州310030 
王永伟 浙江美浓世纪集团杭州310030 
陈广学 华南理工大学 广州 510640 
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中文摘要:
      目的 研究满足面向高保真再现要求的多光谱图像降维方法。 方法 基于二进制小波对信号的分解与人类的视觉特性相匹配,以及非负主成分分析法可较好地保证降维的光谱精度,提出采用基于离散二进制小波变化与非负主成分分析法的综合降维方法,并基于多光谱图像高保真再现的光谱精度、色度精度与变光源色差稳定性的要求,提出采用 CIELAB 的标准色差?Eab 、光谱保真度和平均梯度等 3 个指标来评价降维效果。 结果 经过多光谱图像的测试实验,基于离散小波变换和非负主成分分析法的综合降维方法相对于其他 3 种方法,其光谱精度、色度精度和图像清晰度保持良好。 结论 该方法较好地实现了多光谱图像的高保真再现问题,并且为颜色视觉的认知过程提供了新的理论解释。
英文摘要:
      The current major multispectral image dimensionality reduction methods (principal component analysis, LabPQR, WSPCAplus) cannot meet the need of the multispectral image high-fidelity reproduction. This paper researched a multispectral image dimensionality deduction method for high-fidelity reproduction. Based on the facts that binary wavelet decomposition of the signal matches the human vision characteristics and the nonnegative principal component analysis method can better ensure the spectral accuracy of the dimension reduction image, a composite dimensionality reduction method based on the discrete binary wavelet change and the nonnegative principal component analysis was put forward. Based on the spectral accuracy, chroma precision and chromatic aberration stability of changing light source, the standard color difference of CIELAB, the spectral fidelity and the image average gradient were proposed to evaluate the dimensionality reduction efficacy. After the multispectral image test, the composite dimensionality reduction method based on the discrete wavelet transform and nonnegative principal component analysis could better ensure the spectral accuracy, chroma precision and image definition when compared with the other three methods. This method could better realize the multispectral image high-fidelity production, besides providing a new theoretical explanation for the color vision cognitive process.
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