引用本文:吴成东,卢紫微,于晓升.基于加权随机森林的图像超分辨率算法研究[J].控制与决策,2019,34(10):2243-2248
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基于加权随机森林的图像超分辨率算法研究
吴成东1, 卢紫微2,3, 于晓升1
(1. 东北大学机器人科学与工程学院,沈阳110004;2. 东北大学信息科学与工程学院,沈阳110004;3. 辽宁石油化工大学计算机与通信工程学院,辽宁抚顺113001)
摘要:
针对目前图像超分辨率重建效果欠佳的问题,提出一种基于加权随机森林的图像超分辨率重建算法.利用随机森林对图像块的特征进行聚类,并引入岭回归模型建立每类叶子结点中高、低分辨率图像块的映射关系,重建时根据测试低分辨率图像块所属的类别以及在每类叶子结点中的K近邻近似拟合误差,进行加权预测获得高分辨率图像块.将图像的非局部自相似性与迭代反投影算法相结合对预测的高分辨率图像进行后处理以提高重建质量.实验结果表明,所提出算法可以有效提高峰值信噪比,具有较好的可视效果.
关键词:  超分辨率  随机森林  岭回归  K近邻  加权预测
DOI:10.13195/j.kzyjc.2018.0140
分类号:TP391
基金项目:国家自然科学基金项目(61701101,61603080,U1713216);国家重点机器人工程项目(2017YFB1300900);中央高校基本科研业务费专项资金项目(N172603001,N172604004).
Image super resolution reconstruction algorithm based on weighted random forest
WU Cheng-dong1,LU Zi-wei2,3,YU Xiao-sheng1
(1. Faculty of Robot Science and Engineering,Northeastern University,Shenyang110004,China;2. College of Information Science and Engineering,Northeastern University,Shenyang110004,China;3. College of Computer and Communication Engineering,Liaoning Shihua University,Fushun113001,China)
Abstract:
In order to solve the practical problem of unsatisfactory restored results, an image super resolution reconstruction algorithm via weighted random forest is proposed. Firstly, features of image patches are clustered by the random forest, and ridge regression is introduced to build the mapping between low and high resolution patches for each type of the leaf note. Then high resolution image patch is obtained by weighted prediction based on the cluster which the test low resolution sample belongs to and the K near neighbor approximate fitting error. Finally, the non-local similarity and IBP(Iterative back projection) are utilized to improve the quality of image reconstruction. Experimental results show that the proposed method effectively improves peak signal to noise ratio and acquires better visual effects in reconstructed image.
Key words:  super resolution  random forest  ridge regression  K nearest neighbor  weighted prediction

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