基于相关性准则和参数优化策略的彩色图像灰度化关键技术研究

项目名称： 基于相关性准则和参数优化策略的彩色图像灰度化关键技术研究

项目编号： No.61503176

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 刘且根

作者单位： 南昌大学

项目金额： 20万元

中文摘要： 彩色图像灰度化是将彩色图像转换成灰度图像，它是图像处理和模式识别等技术的预处理阶段中重要的一环。如何挖掘彩色图像中的对比度信息以尽可能地使用灰度图像将彩色图像的信息显示及设计快速稳健的实现算法是其成功的两大关键因素。申请人近年来在相关领域提出了变换域相关性准则（ICCV）及各种基于变量分离的快速算法（TIP和SIAM J IMAGING SCI.），积累了一定成果。为实现高效快速的彩色图像灰度化，项目组拟从三个方向为研究切入点：(1)探讨梯度域相关性准则的基本及广义模型，提出梯度相关性准则衡量灰度化对比度保持能力及融入稀疏范数、通道权重和显著性等先验信息进行灰度化建模；(2)在参数化建立一阶模型后，使用分离变量及离散化策略提出迭代型和全局搜索型快速算法；(3)探讨新的图像灰度化评价指标；三个研究角度相辅相成、共同促使彩色图像对比度在灰度化过程中得到更为丰富的保持，实现高效快速灰度化。

中文关键词： 彩色图像灰度化；对比度保持；梯度相关性；参数优化；线性参数模型

英文摘要： Color-to-gray conversion is process that converts a color image into a grayscale one. It is an important pre-processing stage of image processing and pattern recognition techniques, using the limited range in gray scales to present the input color image contrast as much as possible. How to exploit the color contrast information and design fast and robust algorithm are two issues for its success. In recent years the applicant proposed correlation criterion in transform domain (ICCV) and a variety of fast algorithms based on variable splitting techniques (TIP and SIAM J IMAGING SCI.). To achieve fast and efficient color-to-gray conversion, the project intends to study the three directions: (1) discuss the basic and generalized models based on gradient-domain correlation criterion, trying to use gradient correlation criteria for valuating grayscale contrast maintaining capability and integrate sparse norm, channel weights and saliency information in the model; (2) use variables splitting and discrete strategy to develop iterative and global search algorithms for solving the parameterized first-order model; (3) explore new image decolorization evaluation. The three parts perspectively aim to maintain the color contrast in the process of conversion. The successful implementation of this project will greatly push the color image used in image processing and pattern recognition techniques, and subsequently in the practical applications.

英文关键词： Color-to-gray conversion;contrast-preserving;gradient correlation similarity;parameter optimization;linear parameter model