Colorization method based on the linear relationship assumption  

作　　者：聂栋栋 Wang Li Ma Qinyong 

机构地区：[1]College of Sciences,Yanshan University [2]College of information Science and Engineering,Yanshan University

出　　处：《High Technology Letters》2014年第3期261-266,共6页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61073089);the Joint Funds of the National Natural Science,Foundation of China(No.U1304616);the Qinhuangdao Research&Development Program of Science&Technology(No.2012021A044)

摘　　要：A new local cost function is proposed in this paper based on the linear relationship assumption between the values of the color components and the intensity component in each local image window,then a new quadratic objective function is derived from it and the globally optimal chrominance values can be computed by solving a sparse linear system of equations.Through the colorization experiments on various test images,it is confirmed that the colorized images obtained by our proposed method have more vivid colors and sharper boundaries than those obtained by the traditional method.The peak signal to noise ratio(PSNR) of the colorized images and the average estimation error of the chrominance values relative to the original images also show that our proposed method gives more precise estimation than the traditional method.A new local cost function is proposed in this paper based on the linear relationship assumption between the values of the color components and the intensity component in each local image window,then a new quadratic objective function is derived from it and the globally optimal chrominance values can be computed by solving a sparse linear system of equations.Through the colorization experiments on various test images,it is confirmed that the colorized images obtained by our proposed method have more vivid colors and sharper boundaries than those obtained by the traditional method.The peak signal to noise ratio （PSNR） of the colorized images and the average estimation error of the chrominance values relative to the original images also show that our proposed method gives more precise estimation than the traditional method.

关 键 词：COLORIZATION quadratic objective function optimization least square solver color propagation 

分 类 号：O241.6[理学—计算数学]