Image super resolution reconstruction algorithm based on weighted random forest

被引：0

作者：

Wu C.-D. ^{[1
]}

Lu Z.-W. ^{[2
,3
]}

Yu X.-S. ^{[1
]}

机构：

[1] Faculty of Robot Science and Engineering, Northeastern University, Shenyang

[2] College of Information Science and Engineering, Northeastern University, Shenyang

[3] College of Computer and Communication Engineering, Liaoning Shihua University, Fushun

来源：

Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 10期

关键词：

K nearest neighbor; Random forest; Ridge regression; Super resolution; Weighted prediction;

D O I：

10.13195/j.kzyjc.2018.0140

中图分类号：

学科分类号：

摘要：

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. © 2019, Editorial Office of Control and Decision. All right reserved.

引用

页码：2243 / 2248

页数：5

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