计算机工程与应用2016，Vol.52Issue(23)：208-212,5.DOI:10.3778/j.issn.1002-8331.1605-0312
高斯隶属度优化的超分辨率随机森林学习算法
Random forest learning algorithm for super resolution with Gauss membership opti-mization
摘要
Abstract
Random forest algorithm is an efficient method of single image super resolution, however its decision function is a binary function, and the definitive split on certain image blocks is not the optimal choice. For improving the perfor-mance of single image super resolution, this paper uses Gauss membership functions to build decision functions of deci-sion nodes in random forest, which converts the output values of decision function from definitive values of 0 and 1 to probability values between 0 and 1, then predicts on leaf nodes according to the product of decision nodes’probabilities on the route of the leaf nodes, and then learns the decision parameters in terms of minimum empirical risk metrics, to make the random forest can better learn different sample data. Experimental results show that, by comparing with random forest learning and other popular single image super resolution methods, this method can enhance the peak signal to noise ratio of super resolution images, at the same time has similar efficiency comparable with traditional random forest learn-ing algorithm.关键词
随机森林学习/单图像超分辨率/决策函数/高斯隶属度函数/经验冒险Key words
random forest learning/single image super resolution/decision function/Gauss membership functions/empirical risk分类
信息技术与安全科学引用本文复制引用
周文谊,王吉源..高斯隶属度优化的超分辨率随机森林学习算法[J].计算机工程与应用,2016,52(23):208-212,5.基金项目
江西省教育厅青年科学基金项目（No.GJJ14455）。 （）
