计算机工程与应用Issue(22)：176-180,5.DOI:10.3778/j.issn.1002-8331.1501-0399
基于KNN图层区分的优化式着色算法
Optimization-based colorization based on KNN layers distinction
摘要
Abstract
It is possible to recolorize images by applying existing scribble based colorization algorithms for grayscale images, which omitting colors in original images. This paper proposes an optimized method to improve the existing image recolor-ization technologies. In comparison with the optimization-based colorization, the proposed method features in:(i)using K Nearest Neighbors(KNN)to preprocess images into stratified layers and learn from their content to produce a new sim-ulated weight function;(ii)colorizing images in terms of the optimized layer-based weights and hence producing optimized colorizations. Extensive experimental results show that the proposed algorithm can solve the problem of color leakage at the boundary of the object, and obtain accurate color images. Compared with previous methods, the proposed algorithm is more robust to color blending in the input data.关键词
优化式着色/K最邻近结点算法(KNN)/二次着色/图层信息Key words
optimization-based colorization/K Nearest Neighbors(KNN)/recolorization/layers information分类
信息技术与安全科学引用本文复制引用
盛家川,杨巍..基于KNN图层区分的优化式着色算法[J].计算机工程与应用,2015,(22):176-180,5.基金项目
国家自然科学基金（No.61502331）；天津市应用基础与前沿技术研究计划（No.15JCQNJC00800，No.15JCYBJC16000）；天津市高等学校科技发展基金计划项目（No.20140816）；天津财经大学优秀青年学者计划（No.YQ1506）；天津财经大学2014年度研究生科研资助计划（No.2014TCS02）。 （）
