Single image super-resolution reconstruction using support vector regression

Qi-ping YUAN; Hai-jie LIN; Zhi-hong CHEN; Xiao-ping YANG

doi:10.3788/OPE.20162409.0001

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Single image super-resolution reconstruction using support vector regression

Information Sciences | 更新时间：2020-07-07

- Single image super-resolution reconstruction using support vector regression
- Optics and Precision Engineering Vol. 24, Issue 9, Pages: 2302-2309(2016)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20162409.0001
  CLC： TP391.4
- Received：13 June 2016，
  
  Accepted：19 July 2016，
  
  Published：2016-09
- 稿件说明：
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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：

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.

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references

穆绍硕, 张叶, 贾平.基于自学习局部线性嵌入的多幅亚像元超分辨成像[J].光学精密工程, 2015, 23(9):2677-2686.

MU SH SH, ZHANG Y, JIA P. Super-resolution imaging of multi-frame sub-pixel images based on self-learning LLE[J].Opt. Precision Eng., 2015, 23(9):2677-2686. (in Chinese)

贾苏娟, 韩广良, 陈小林, 等.光度非均匀彩色序列图像超分辨重建[J].液晶与显示, 2014, 29(1):106-113.

JIA S J, HAN G L, CHEN X L, et al.. Super-resolution reconstruction with photometric change of color image sequence[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(1):106-113.(in Chinese)

何林阳, 刘晶红, 李刚, 等.改进BRISK特征的快速图像配准算法[J].红外与激光工程, 2014, 43(8):2722-2727.

HE L Y, LIU J H, LI G, et al.. Fast image registration approach based on improved BRISK[J]. Infrared and Laser Engineering, 2014, 43(8):2722-2727.(in Chinese)

邓承志, 田伟, 汪胜前, 等.近似稀疏正则化的红外图像超分辨率重建[J].光学精密工程, 2014, 22(6):1648-1654.

DENG CH ZH, TIAN W, WANG SH Q, et al.. Super-resolution reconstruction of approximate sparsity regularized infrared images[J].Opt. Precision Eng., 2014, 22(6):1648-1654.(in Chinese)

YANG J C, WANG Z W, LIN Z, et al..Coupled dictionary training for image super-resolution[J]. IEEE Transactions on Image Processing, 2012, 21(8):3467-3478.

REN C, HE X H, TENG Q Z, et al..Single image super-resolution using local geometric duality and non-local similarity[J]. IEEE Transactions on Image Processing, 2016, 25(5):2168-2183.

龚卫国, 潘飞宇, 李进明.用双层重建法实现单幅图像的超分辨率重建[J].光学精密工程, 2014, 22(3):720-729.

GONG W G, PAN F Y, LI J M. Single-image super-resolution reconstruction via double layer reconstructing[J]. Opt. Precision Eng., 2014, 22(3):720-729.(in Chinese)

ZHANG K B, GAO X B, LI J, et al..Single image super-resolution using regularization of non-local steering kernel regression[J]. Signal Processing, 2016, 123:53-63.

LI J M, QU Y Y, LI C H, et al..Image super-resolution based on multi-kernel regression[J]. Multimedia Tools and Applications, 2016, 75:4115-4128.

NI K S, NGUYEN T Q.Image super-resolution using support vector regression[J].IEEE Transactions on Image Processing, 2007, 16(6):1596-1610.

YANG M C, WANG Y C F. A self-learning approach to single image super-resolution[J]. IEEE Transactions on Multimedia, 2013, 15(3):498-508.

DONG C, CHEN C L, HE K M, et al..Image super-resolution using deep convolutional networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307.

LIANG Y D, WANG J J, ZHOU S P, et al..Incorporating image priors with deep convolutional neural networks for image super-resolution[J]. Neurocomputing, 2016, 194:340-347.

TIMOFTE R, DE S, VAN G L. A+:adjusted anchored neighborhood regression for fast super-resolution[C].Proceeding of the 12th Asian Conference on Computer Vision, 2014:111-26.

PELEG T, ELAD M. A statistical prediction model based on sparse representations for single image super-resolution[J]. IEEE Transactions on Image Processing, 2014, 23(6):2569-2582.

孟伟, 金龙旭, 李国宁, 等.调制传递函数在遥感图像复原中的应用[J].红外与激光工程, 2014, 43(5):1690-1696.

MENG W, JIN L X, LI G N, et al..Application of MTF in remote sensing image restoration[J]. Infrared and Laser Engineering, 2014, 43(5):1690-1696.(in Chinese)

ZHAO G Y, AHONEN T, MATAS J, et al.. Rotation-invariant image and video description with local binary pattern features[J]. IEEE Transactions on Image Processing, 2012, 21(4):1465-1477.

CHEN P H, LIN C J, SCHOLKOPF B. A tutorial on v-support vector machines[J].Applied Stochastic Models in Business and Industry, 2005, 21(2):111-136.

CHANG C C, LIN C J. LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology, 2011, 2(3).

张振东, 陈健, 王伟国, 等.基于SSIM_NCCDFT的超分辨率的复原评价方法研究[J].液晶与显示, 2015, 30(4):713-721.

ZHANG ZH D, CHEN J, WANG W G, et al..Evaluation method of super-resolution restoration based on SSIM_NCCDFT[J]. Chinese Journal of Liquid Crystals and Displays, 2015, 30(4):713-721.(in Chinese)

KIM K I, KWON Y. Single-image super-resolution using sparse regression and natural image prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(6):1127-1133.

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