t-SNE最大化的自适应彩色图像灰度化方法  

Adaptive decolorization method based on t-SNE maximization

作　　者：谢斌[1] 徐燕 王冠超 杨舒敏 李燕伟 Xie Bin;Xu Yan;Wang Guanchao;Yang Shumin;Li Yanwei(College of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学信息工程学院,赣州341000

出　　处：《中国图象图形学报》2024年第8期2333-2349,共17页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61872429);江西省自然科学基金项目(20192BAB207036);江西理工大学博士启动基金项目(20520010058)。

摘　　要：目的 彩色图像的灰度化是计算机视觉领域的研究热点。针对传统彩色图像灰度化方法得到的灰度图像存在对比度保持不足、细节模糊及层次感欠缺等问题,本文结合t分布随机邻域嵌入(t-distributed stochastic neighbor embedding,t-SNE)提出了一种更加简单、高效的彩色图像灰度化新方法。方法 首先,将t-SNE降维思想引入到彩色图像的灰度化过程中,设计了一种基于t-SNE最大化的彩色图像灰度化新模型,通过最大化能量函数使原始彩色图像中对比度较小的区域在灰度化后其对比度能够适当地变大或保持,让灰度图像更好地保持原始彩色图像的对比度特征和层次感。其次,在新模型中设计了一种自适应的对比度保持策略,根据颜色对比度信息来自适应地调节原始彩色图像不同区域的灰度化力度,更好地保留原始彩色图像的细节和对比度信息。最后,采用了一种高效的离散搜索方法以快速求解所提新模型。结果 基于Cadik、CSDD(complex scene decolorization dataset)和Color250数据集的大量实验结果表明,与传统方法相比,本文方法得到的灰度图像具有更好的表现,在颜色对比度保持率(color contrast preserving ratio, CCPR)指标上,本文方法在上述3个数据集上的平均CCPR值最高,分别为0.874、0.862和0.864。另外,在相同硬件上测试不同灰度化方法的运行效率时,本文方法的运行时间最短。结论 相较于传统灰度化方法,本文方法不仅能够更好地保持原始彩色图像的对比度、细节特征和层次感,而且在主观评价和客观评价方面均有更好的表现。Objective Image decolorization converts a color image into a grayscale one.It aims to maintain important fea⁃tures of the original color image as much as possible.It is normally used in the preprocessing stage of pattern recognition and image processing systems,such as extracting fingerprint features,face recognition,and medical imaging.Given that image decolorization maps color images in three dimensions into grayscale images in one dimension,it is a dimensionality reduction process essentially.Thus,the loss of information is inevitable.Accordingly,the major task for researchers can be expressed as preserving important features such as contrast,detail information,and hierarchy of the original color image as much as possible.In the process of acquiring grayscale images,the most basic method is to extract the luminance channel of color images directly.However,acquiring only the luminance information tends to lose the contrast details and appearance features of the input image.In recent years,researchers have focused more on whether grayscale images match the perception of color of the human visual system,and many classical image decolorization algorithms have been pro⁃posed.These color-to-gray conversion methods generally fall into two main categories:local mapping methods and global mapping methods.In the local mapping method,the mapping function is spatially varying,that is,the mapping functions of pixels at different spatial locations often differ.The grayscale image obtained by the local mapping method can keep part of the image structure information and local contrast information.However,such methods tend to lose the overall informa⁃tion of the image.They can also produce certain phenomena such as halo and noise enhancement.Some researchers have proposed the global mapping method to address the shortcomings of the abovementioned methods.In the global mapping method,the same mapping function is used for the entire image,and the grayscale value is only related to the color of the original color image.Howe

关 键 词：彩色图像灰度化 t分布随机邻域嵌入(t-SNE) 对比度保持 离散搜索 细节保持 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]