[1]刘 哲,韩九强,黄世奇.基于多引导滤波器的单幅图像超分辨率技术[J].红外技术,2017,39(10):920-927.[doi:10.11846/j.issn.1001_8891.201710009]
 LIU Zhe,HAN jiuqiang,HUANG Shiqi.Single Image Super-Resolution Based on Multi-Guided Filtering[J].Infrared Technology,2017,39(10):920-927.[doi:10.11846/j.issn.1001_8891.201710009]
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基于多引导滤波器的单幅图像超分辨率技术
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
39卷
期数:
2017年第10期
页码:
920-927
栏目:
出版日期:
2017-10-20

文章信息/Info

Title:
Single Image Super-Resolution Based on Multi-Guided Filtering
文章编号:
1001-8891(2017)10-0920-08
作者:
刘 哲12韩九强2黄世奇1
1. 西京学院电子信息工程系,陕西 西安 710123;2. 西安交通大学电信学院计算机科学与技术系,陕西 西安710049
Author(s):
LIU ZheHAN jiuqiangHUANG Shiqi
1. Department of Electronic and Information Engineering, Xijing University, Xi’an 710123, China;
2. Department of Telecommunication, Xi’an Jiaotong University, Xi’an 710049, China
关键词:
超分辨率引导滤波器样本训练库高频细节
Keywords:
super resolutionguided filteringexemplar training databasehigh frequency detail
分类号:
TP391.41
DOI:
10.11846/j.issn.1001_8891.201710009
文献标志码:
A
摘要:
提出了一种基于多引导滤波器的单幅图像超分辨率方法。首先,该方法通过大量的自然图像建立高低分辨率图像块样本训练库,并通过聚类算法将具有相似性质的高低分辨率样本块进行聚类;其次,将输入低分辨率图像进行重叠分块,并在样本库中搜索最近邻的高低分辨率样本聚类;再次,将输入低分辨率图像块作为输入图像,与样本库中最近邻的低分辨率聚类样本作为引导图像,运用本文提出的多引导滤波器计算引导滤波器的参数;最后,利用样本库中最近邻的高分辨率聚类样本和引导滤波器的参数,通过多引导滤波器就可以重构高分辨率图像。实验结果表明,本文算法不仅能很好地重构图像的高频细节,还能很好地恢复图像的纹理特征。
Abstract:
In this paper, a single image super-resolution method based on multi-guided filtering is proposed. First, an exemplar training database consisting of pairs of low-resolution and corresponding high-resolution image patches is constructed using many natural images. High- and low-resolution image patches with similar properties are clustered using a clustering algorithm. Next, the low-resolution input image is divided into overlapping patches, and the nearest neighbor high-and low-resolution sample cluster is searched against the exemplar training database. Then, the low-resolution input image patch is used as the new input; the nearest neighbor low-resolution clustering sample is used as the guide image. The multi-guided filter is used to calculate the parameters of the guide filter. Finally, the high-resolution images can be reconstructed with the multi-guided filter using the nearest neighbor high-resolution clustering samples and the parameters of the multi-guide in the sample bank. Experimental results show that the proposed algorithm not only reconstructs the high-frequency detail of an image, but also recovers the texture features.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-01-25;修订日期:2017-03-06.
作者简介:刘哲(1972-),男,教授级高工,博士,研究方向为机器视觉、人工智能及模式识别。
基金项目:国家自然科学基金(61473237)。
更新日期/Last Update: 2017-10-20