用支持向量回归法实现单帧图像超分辨率重建

袁其平; 林海杰; 陈志宏; 杨晓苹

doi:10.3788/OPE.20162409.0001

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用支持向量回归法实现单帧图像超分辨率重建

信息科学 | 更新时间：2020-07-07

- 用支持向量回归法实现单帧图像超分辨率重建
- Single image super-resolution reconstruction using support vector regression
- 光学精密工程 2016年24卷第9期页码：2302-2309
- 作者机构：
  
  天津理工大学天津市薄膜电子与通信器件重点实验室, 天津 300384
- 作者简介：
  
  [ "林海杰(1991-), 男, 山西吕梁人, 硕士研究生, 2014年于山西师范大学获得学士学位, 主要研究图像识别, 图像重构, 模式识别的研究。E-mail:haijie_lin@126.com" ]
  陈志宏(1978-), 男, 天津人, 博士, 讲师, 1996年于大连理工大学获得学士学位, 2004年、2008年于天津大学分别获得硕士、博士学位, 主要研究图像处理, 模式识别等方面的研究。E-mail:chenzhihong_tjut@163.com CHEN Zhi-hong, E-mail:chenzhihong_tjut@163.com
- 基金信息：
  
  天津市应用基础与前沿技术研究计划青年基金资助项目(14JCQNJC00900)
- DOI：10.3788/OPE.20162409.0001
  中图分类号： TP391.4
- 收稿日期：2016-06-13，
  
  录用日期：2016-7-19，
  
  纸质出版日期：2016-09
- 稿件说明：
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袁其平, 林海杰, 陈志宏, 等. 用支持向量回归法实现单帧图像超分辨率重建[J]. 光学精密工程, 2016,24(9):2302-2309.

Qi-ping YUAN, Hai-jie LIN, Zhi-hong CHEN, et al. Single image super-resolution reconstruction using support vector regression[J]. Optics and precision engineering, 2016, 24(9): 2302-2309.
袁其平, 林海杰, 陈志宏, 等. 用支持向量回归法实现单帧图像超分辨率重建[J]. 光学精密工程, 2016,24(9):2302-2309. DOI： 10.3788/OPE.20162409.0001.

Qi-ping YUAN, Hai-jie LIN, Zhi-hong CHEN, et al. Single image super-resolution reconstruction using support vector regression[J]. Optics and precision engineering, 2016, 24(9): 2302-2309. DOI： 10.3788/OPE.20162409.0001.

摘要

由于一些传统的超分辨率重建算法学习多幅不同类别的图像仍无法获得好的重建效果，本文提出了一种基于支持向量回归机和光栅扫描的单帧图像超分辨率重建算法。该算法首先采用光栅扫描法对一组高低分辨率训练图像提取图像块，从块中分别抽取输入向量和标签像素。利用Log算子判断这些块是属于高频空间还是低频空间，从而构建高低频空间向量对并对其进行优化。然后，用支持向量回归机（SVR）工具训练优化后的向量对，得到高低频空间下的两个字典；抽取测试低分辨率图像中的块并得到高低频空间下的输入向量，利用SVR工具回归对应的属于超分辨率图像块的标签像素并得到回归后的图像。最后，对图像进行后处理得到最终的超分辨率图像。与其它算法的对比实验表明：提出的算法具有较好的视觉效果。特别在放大倍数为2时，提出的算法在不同图像上的峰值信噪比（PSNR）和结构相似度（SSIM）值较双三次插值法分别提高了3.1%~5.3%和1.5%~8.1%。得到的结果显示提出的算法获得了更好的重建效果。

Abstract

Some of the traditional single-frame super-resolution (SR) reconstruction algorithms can not get good reconstruction results

although they learns many different types of images. Therefore

a super-resolution method combined with the Support Vector Regression (SVR) and raster-scan actions was proposed. Firstly

image patches were extracted from a group of high resolution (HR) images and the corresponding low resolution (LR) edition by the raster-scan actions

and input vectors and pixel vectors were taken out from the patches. The Log algorithm was used to determine that those patches were belong to high-frequency space or low frequency space then to construct the high and low frequency vector pairs. Then

those optimized vector pairs were trained by the SVR and two dictionaries in high/low frequency spaces were built eventually. Furthermore

input vectors were extracted from tested LR images in high/low frequency space

and the SVR tool was used to predict the SR pixel labels and the predicted pixels were added to bicubic interpolation image based on LR edition. Finally

the SR image was obtained by post-processing the previous image. In comparison with other algorithms

experimental results indicate that the proposed method provides good visual effects. It enhances its Peak Signal-to-Noise Ration (PSNR) and Structural Similarity Index Measurement (SSIM) by 3.1%-5.3% and 1.5%-8.1% on different images

respectively as compared with bicubic interpolation method.

关键词

Keywords

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