基于字典学习和非局部相似的超分辨率重建

doi:10.11772/j.issn.1001-9081.2014.11.3300

计算机应用 ›› 2014, Vol. 34 ›› Issue (11): 3300-3303.DOI: 10.11772/j.issn.1001-9081.2014.11.3300

基于字典学习和非局部相似的超分辨率重建

首照宇¹,吴广祥¹,陈利霞²

1. 桂林电子科技大学信息与通信学院,广西桂林 541004
2. 桂林电子科技大学数学与计算科学学院,广西桂林 541004

收稿日期:2014-05-14 修回日期:2014-07-02 发布日期:2014-12-01 出版日期:2014-11-01
通讯作者: 吴广祥
作者简介:首照宇(1974-),男,重庆人,副教授,主要研究方向:图像处理、模式识别;吴广祥(1988-),男,广西玉林人,硕士研究生,主要研究方向:数字图像处理;陈利霞(1979-),女,湖北黄冈人,副教授,博士,主要研究方向:数字图像处理、小波分析、偏微分方程。
基金资助:
江苏高校优势学科建设工程项目;广西自然科学基金资助项目;广西科学研究与技术开发计划项目;桂林电子科技大学研究生教育创新计划项目;桂林电子科技大学研究生教育创新计划项目

Super-resolution reconstruction based on dictionary learning and non-local similarity

SHOU Zhaoyu¹,WU Guangxiang¹,CHEN Lixia²

1. School of Information and Communication, Guilin University of Electronic Technology, Guilin Guangxi 541004, China;
2. School of Mathematics and Computer Science, Guilin University of Electronic Technology, Guilin Guangxi 541004, China

Received:2014-05-14 Revised:2014-07-02 Online:2014-12-01 Published:2014-11-01
Contact: WU Guangxiang

摘要/Abstract

摘要：

为提高单帧降质图像的分辨率,提出了一种基于字典学习和非局部相似性的超分辨率重建算法。该算法主要将高分辨率图像减去利用迭代反投影重建结果得到差值图像,再利用K-奇异值分解(K-SVD)算法和联合字典生成的思想形成的字典训练方法,训练差值图像块和低分辨率图像块得到对应的高、低分辨率字典用于超分辨重建。此外,引入非局部相似性的正则项约束以提高重建图像的质量。实验结果表明,所提算法重建得到的图像在主观视觉效果和客观评价上优于基于例子学习的超分辨率算法。

Abstract:

To deal with the single-image scale-up problem, a super-resolution reconstruction algorithm based on dictionary learning and non-local similarity was proposed. The difference images between the high-resolution images and results of using iterative back-projection image reconstruction were obtained, and then the high and corresponding low dictionaries could be co-generated by training difference image patches and the corresponding low-resolution image patches via using K-Singular Value Decomposition (K-SVD) algorithm which was combined with the idea that the high and low dictionaries could be co-trained for super-resolution reconstruction. In addition, a non-local similarity regularization constraint was introduced in the new algorithm to further improve the quality of the reconstructed images. The experimental results show that the proposed algorithm achieves better results than learning-based algorithms in terms of both visual perception and objective evaluation.

中图分类号:

TP391.41

首照宇吴广祥陈利霞. 基于字典学习和非局部相似的超分辨率重建[J]. 计算机应用, 2014, 34(11): 3300-3303.

SHOU Zhaoyu WU Guangxiang CHEN Lixia. Super-resolution reconstruction based on dictionary learning and non-local similarity[J]. Journal of Computer Applications, 2014, 34(11): 3300-3303.

参考文献

[1]HA T, TINNEFELD P. Photophysics of fluorescent probes for single-molecule biophysics and super-resolution imaging[J]. Annual Review of Physical Chemistry, 2012, 63: 595-617.
[2]LEE C-F, HUANG Y-L. An efficient image interpolation increasing payload in reversible data hiding[J]. Expert Systems with Applications, 2012, 39(8): 6712-6719.
[3]RASTI P, DEMIREL H, ANBARJAFARI G. Image resolution enhancement by using interpolation followed by iterative back projection[C]// Proceedings of the 21st Signal Processing and Communications Applications Conference. Piscataway: IEEE Press, 2013: 1-4.
[4]HEYLEN R, AKHTER M A, SCHEUNDERS P. On using projection onto convex sets for solving the hyperspectral unmixing problem[J]. IEEE Geoscience and Remote Sensing Letters, 2013,10(6):1522-1526.
[5]ZHANG L, ZHANG H, SHEN H, et al. A super-resolution reconstruction algorithm for surveillance images[J]. Signal Processing, 2010, 90(3): 848-859.
[6]FREEMAN W T, JONES T R, PASZTOR E C. Example-based super-resolution[J]. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
[7]YANG J, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.
[8]YANG J, WRIGHT J, HUANG T, et al. Image super-resolution as sparse representation of raw image patches[C]// Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2008: 1-8.
[9]ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C]// Proceedings of the 7th International Conference on Curves and Surfaces, LNCS 6920. Berlin: Springer-Verlag, 2012: 711-730.
[10]ZHANG K, GAO X, LI X, et al. Partially supervised neighbor embedding for example-based image super-resolution[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 230-239.
[11]LIANG L, CHIU K H, LAM E Y. Fast single frame super-resolution using scale-invariant self-similarity[C]// Proceedings of the 2013 IEEE International Symposium on Circuits and Systems. Piscataway: IEEE Press, 2013: 1191-1194.
[12]DONG W, ZHANG L, SHI G, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing, 2011, 20(7): 1838-1857.
[13]ZHANG K, GAO X, TAO D, et al. Single image super-resolution with non-local means and steering kernel regression [J]. IEEE Transactions on Image Processing, 2012, 21(11): 4544-4556.
[14]SMITH L N, OLSON C C, JUDD K P, et al. Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm[J]. Applied Optics, 2012, 51(17): 3941-3949.
[15]WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

基于字典学习和非局部相似的超分辨率重建

Super-resolution reconstruction based on dictionary learning and non-local similarity

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