[1]黄宇达,王迤冉,牛四杰.引入细节约束因子的半耦合字典学习超分辨率重构模型[J].郑州大学学报(工学版),2021,42(03):59.[doi:10.13705/j.issn.1671-6833.2021.03.010]
　Huang Yuda,Wang Yanran,Niu Sijie,et al.Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor[J].Journal of Zhengzhou University (Engineering Science),2021,42(03):59.[doi:10.13705/j.issn.1671-6833.2021.03.010]
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年03期
页码:
59
栏目:
出版日期:
2021-05-10

文章信息/Info

Title:
Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor
作者:
黄宇达;  王迤冉 ;  牛四杰
周口职业技术学院信息工程学院;周口师范学院网络工程学院;济南大学信息科学与工程学院;
Author(s):
Huang Yuda; Wang Yanran; Niu Sijie;
College of Information Engineering of Zhoukou Vocational and Technical College; School of Network Engineering, Zhoukou Teachers College; School of Information Science and Engineering, Jinan University;
关键词:
超分辨率; 半耦合字典学习; 细节约束因子; 拉普拉斯分布
Keywords:
super-resolution;  semi-coupled dictionary learning;  detail constraint factor;  Laplacian distribution
DOI:
10.13705/j.issn.1671-6833.2021.03.010
文献标志码:
A
摘要:
为了提升单幅图像的超分辨率重构细节,提出了一种基于细节保持的超分辨率重构方法针对半耦合字典学习超分辨率算法细节保持不够高的缺陷,采用细节约束因子与半耦合字典交替学习策略在重构阶段,利用图像水平方向与垂直方向的梯度构建细节约束因子,并引入到半耦合字典学习框架,进一步提高重构精度为了改进细节约束因子在重构过程中的贡献度,采用边界差异的拉普拉斯分布实现参数的自适应选择相比于半耦合字典学习超分辨率算法,该方法在峰值信噪比方面平均提升1.5%实验结果表明,该方法在主观和客观评价标准下均取得了较好的重构效果,提升了超分辨率重构质量
Abstract:
In order to improve the super-resolution reconstruction quality of single image, an improved learning based super-resolution approach was proposed in this paper. To tackle the problem of low details of semi-coupled dictionary learning super-resolution algorithm, the paper presented learning strategy where detail constraint factor and semi-coupled dictionary learning were performed in turn. In reconstruction stage, detail constraint factor was designed by the gradient in both horizontal and vertical direction. Combined with semi-coupled dictionary learning, detail constraint factor was used to further improve the super-resolution reconstruction quality. In order to improve the contribution of detail constraint factor on preserving boundary information, the adaptive regular parameter was explored via the approximate Laplacian distribution of edge difference. Compared with the semi coupled dictionary learning super-resolution algorithm, the peak signal-to-noise ratio of this method was increased by 1.5% on average. Experiments demonstrated that the proposed method could achieve better reconstruction effect in both subjective and objective evaluation and improve the quality of super-resolution.

参考文献/References:

[1] BAKER S,KANADE T.Limits on super-resolution and how to break them[J].IEEE transactions on pattern analysis and machine intelligence,2002,24(9):1167-1183.

[2] 王雪,隋立春,杨振胤,等.最优方向耦合字典学习的遥感影像超分辨率重建[J].计算机工程与应用,2018,54(7):201-205.
[3] TRINH D H,LUONG M,DIBOS F,et al.Novel example-based method for super-resolution and denoising of medical images[J].IEEE transactions on image processing,2014,23(4):1882-1895.
[4] MICHAELI T,ELDAR Y C.A hybrid vector Wiener filter approach to translational super-resolution[J]. IEEE transactions on image processing,2010,1:1-18.
[5] 毛晓波,张志超.基于二维经验模态分解的单幅图像超分辨率重建[J].郑州大学学报(工学版),2014,35(5):15-18.
[6] MARQUINA A,OSHER S J.Image super-resolution by TV-regularization and bregman iteration[J].Journal of scientific computing,2008,37(3):367-382.
[7] 徐志刚,李文文,朱红蕾,等.一种基于L1/2正则约束的超分辨率重建算法[J].华中科技大学学报(自然科学版),2017,45(6):38-42.
[8] SUN J,XU Z B,SHUM H Y.Image super-resolution using gradient profile prior[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2008:1-8.
[9] FREEMAN W T, PASZTOR E C, CARMICHAEL O T. Learning low-level vision[J]. International journal of computer vision, 2000, 40(1):25-47.
[10] FREEMAN W T,JONES T R,PASZTOR E C.Example-based super-resolution[J].IEEE computer graphics and applications,2002,22(2):56-65.
[11] SUN J,ZHENG N N,TAO H,et al.Image hallucination with primal sketch priors[C]//2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003 Proceedings.Piscataway:IEEE,2003:729-736.
[12] CHANG H,YEUNG D Y,XIONG Y M.Super-resolution through neighbor embedding[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004. Piscataway:IEEE,2004:1-8.
[13] YANG J C,WRIGHT J,HUANG T,et al.Image super-resolution as sparse representation of raw image patches[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2008:1-8.
[14] YANG J C,WRIGHT J,HUANG T S,et al.Image super-resolution via sparse representation[J].IEEE transactions on image processing,2010,19(11):2861-2873.
[15] ZEYDE R,ELAD M,PROTTER M.On single image scale-up using sparse-representations[C]//7th International Conference on Curves and Surfaces, Curves and Surfaces 2010.Cham:Springer,2012:711-730.
[16] AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE transactions on signal processing,2006,54(11):4311-4322.
[17] ENGAN K,AASE S O,HAKON HUSOY J.Method of optimal directions for frame design[C]//1999 IEEE International Conference on Acoustics,Speech,and Signal Processing.Proceedings.Piscataway:IEEE,1999:2443-2446.
[18] 王杰,李胜光,宋一帆,等.图像去模糊的自适应交替方向乘子重叠组稀疏方法[J].郑州大学学报(工学版),2018,39(5):52-57,78.
[19] WANG S L,ZHANG L,LIANG Y,et al.Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE,2012:2216-2223.
[20] 杨爱萍,钟腾飞,何宇清.基于非局部相似性和分类半耦合字典学习的超分辨率重建[J].天津大学学报(自然科学与工程技术版),2015,48(1):87-94.
[21] 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.
[22] DONG C,LOY C C,HE K M,et al.Learning a deep convolutional network for image super-resolution[C]// European Conference on Computer Vision, Computer vision-ECCV 2014. Cham:Springer,2014:184-199.

备注/Memo

备注/Memo:
更新日期/Last Update: 2021-06-24