基于层次聚类的图像超分辨率重建 下载: 924次
曾台英, 杜菲. 基于层次聚类的图像超分辨率重建[J]. 光学学报, 2018, 38(4): 0410004.
Taiying Zeng, Fei Du. Image Super-Resolution Reconstruction Based on Hierarchical Clustering[J]. Acta Optica Sinica, 2018, 38(4): 0410004.
[1] 王晓文, 刘雨. 图像超分辨率研究综述[J]. 信息技术, 2009( 7): 236- 240.
王晓文, 刘雨. 图像超分辨率研究综述[J]. 信息技术, 2009( 7): 236- 240.
Wang XW, LiuY. Survey of image super-resolution investigation[J]. Information Technology, 2009( 7): 236- 240.
Wang XW, LiuY. Survey of image super-resolution investigation[J]. Information Technology, 2009( 7): 236- 240.
[2] 陆婉芸, 王继周. 遥感影像超分辨率处理方法与研究进展[J]. 测绘科学, 2016, 41(12): 53-58, 69.
陆婉芸, 王继周. 遥感影像超分辨率处理方法与研究进展[J]. 测绘科学, 2016, 41(12): 53-58, 69.
Lu W Y, Wang J Z. Survey of super resolution processing method of remote sensing image[J]. Science of Surveying and Mapping, 2016, 41(12): 53-58, 69.
[3] 席志红, 曾继琴, 李爽. 基于双字典和稀疏表示的医学图像超分辨率重建[J]. 计算机测量与控制, 2017, 25(3): 197-200.
席志红, 曾继琴, 李爽. 基于双字典和稀疏表示的医学图像超分辨率重建[J]. 计算机测量与控制, 2017, 25(3): 197-200.
Xi Z H, Zeng J Q, Li S. Super-resolution reconstruction of medical image based on dual-dictionary and sparse representation[J]. Computer Measurement & Control, 2017, 25(3): 197-200.
[4] 廖秀秀. 基于学习的图像超分辨率重建算法研究[D]. 广州: 华南理工大学, 2013.
廖秀秀. 基于学习的图像超分辨率重建算法研究[D]. 广州: 华南理工大学, 2013.
Liao XX. Research on learning-based image super-resolution reconstruction algorithms[D]. Guangzhou: South China University of Technology, 2013.
Liao XX. Research on learning-based image super-resolution reconstruction algorithms[D]. Guangzhou: South China University of Technology, 2013.
[5] 谭政, 相里斌, 吕群波, 等. 一种基于频域的序列图像超分辨率增强方法[J]. 光学学报, 2017, 37(7): 0710001.
谭政, 相里斌, 吕群波, 等. 一种基于频域的序列图像超分辨率增强方法[J]. 光学学报, 2017, 37(7): 0710001.
[6] 郑向涛, 袁媛, 卢孝强. 自外而内的单幅图像超分辨率复原算法[J]. 光学学报, 2017, 37(3): 0318006.
郑向涛, 袁媛, 卢孝强. 自外而内的单幅图像超分辨率复原算法[J]. 光学学报, 2017, 37(3): 0318006.
[7] Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution[J]. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution[J]. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
[8] Yang JC, WrightJ, HuangT, et al. Image super-resolution as sparse representation of raw image patches[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 10139952.
Yang JC, WrightJ, HuangT, et al. Image super-resolution as sparse representation of raw image patches[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 10139952.
[11] 许晓丽. 基于聚类分析的图像分割算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
许晓丽. 基于聚类分析的图像分割算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
Xu XL. Research of image segmentation algorithm based on clustering analysis[D]. Harbin: Harbin Engineering University, 2012.
Xu XL. Research of image segmentation algorithm based on clustering analysis[D]. Harbin: Harbin Engineering University, 2012.
[12] 卢汉清, 桂创华, 刘静. 基于层次聚类的图像检索方法: CN200810240361.8[P].2010-06-23.
卢汉清, 桂创华, 刘静. 基于层次聚类的图像检索方法: CN200810240361.8[P].2010-06-23.
Lu HQ, Gui CH, Liu J. Image retrieval method based on hierarchical clustering: CN200810240361.8[P].2010-06-23.
Lu HQ, Gui CH, Liu J. Image retrieval method based on hierarchical clustering: CN200810240361.8[P].2010-06-23.
[13] 黄文奇, 许如初. 近世计算理论导引: NP难度问题的背景、前景及其求解算法研究[M]. 北京: 科学出版社, 2006.
黄文奇, 许如初. 近世计算理论导引: NP难度问题的背景、前景及其求解算法研究[M]. 北京: 科学出版社, 2006.
Huang WQ, Xu RC. In the theory of computation: Research background, foreground and algorithm for solving NP hard problems[M]. Beijing: Science Press, 2006.
Huang WQ, Xu RC. In the theory of computation: Research background, foreground and algorithm for solving NP hard problems[M]. Beijing: Science Press, 2006.
[15] Elad M, Yavneh I. A plurality of sparse representations is better than the sparsest one alone[J]. IEEE Transactions on Information Theory, 2009, 55(10): 4701-4714.
Elad M, Yavneh I. A plurality of sparse representations is better than the sparsest one alone[J]. IEEE Transactions on Information Theory, 2009, 55(10): 4701-4714.
[17] SunJ, Xu ZB, Shum HY. Image super-resolution using gradient profile prior[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 10139964.
SunJ, Xu ZB, Shum HY. Image super-resolution using gradient profile prior[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2008: 10139964.
[19] 张小丹, 范九伦, 徐健, 等. K均值聚类和支持向量数据描述的图像超分辨率算法[J]. 中国图象图形学报, 2016, 21(2): 135-144.
张小丹, 范九伦, 徐健, 等. K均值聚类和支持向量数据描述的图像超分辨率算法[J]. 中国图象图形学报, 2016, 21(2): 135-144.
Zhang X D, Fan J L, Xu J, et al. Image super-resolution algorithm via K-means clustering and support vector data description[J]. Journal of Image and Graphics, 2016, 21(2): 135-144.
[20] 虞涛. 基于邻域嵌入的图像超分辨率重建研究[D]. 南京: 南京邮电大学, 2013.
虞涛. 基于邻域嵌入的图像超分辨率重建研究[D]. 南京: 南京邮电大学, 2013.
YuT. Research on neighbor embedding base image super-resolution reconstruction[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2013.
YuT. Research on neighbor embedding base image super-resolution reconstruction[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2013.
[21] 方琪. 基于字典学习的图像超分辨率重建算法及研究[D]. 合肥: 合肥工业大学, 2016.
方琪. 基于字典学习的图像超分辨率重建算法及研究[D]. 合肥: 合肥工业大学, 2016.
FangQ. Research for image super resolution reconstruction based on dictionary learning[D]. Hefei: Hefei University of Technology, 2016.
FangQ. Research for image super resolution reconstruction based on dictionary learning[D]. Hefei: Hefei University of Technology, 2016.
[22] 王德芬, 高建强, 李莉. 基于中值PCA和加权PCA数据分类的研究[J]. 信息技术, 2014( 2): 14- 18.
王德芬, 高建强, 李莉. 基于中值PCA和加权PCA数据分类的研究[J]. 信息技术, 2014( 2): 14- 18.
Wang DF, Gao JQ, LiL. Studies on data classification based on median PCA and weighted PCA[J]. Information Technology, 2014( 2): 14- 18.
Wang DF, Gao JQ, LiL. Studies on data classification based on median PCA and weighted PCA[J]. Information Technology, 2014( 2): 14- 18.
[24] MairalJ, BachF, PonceJ, et al. Non-local sparse models for image restoration[C]. IEEE 12 th International Conference on Computer Vision , 2010: 2272- 2279.
MairalJ, BachF, PonceJ, et al. Non-local sparse models for image restoration[C]. IEEE 12 th International Conference on Computer Vision , 2010: 2272- 2279.
曾台英, 杜菲. 基于层次聚类的图像超分辨率重建[J]. 光学学报, 2018, 38(4): 0410004. Taiying Zeng, Fei Du. Image Super-Resolution Reconstruction Based on Hierarchical Clustering[J]. Acta Optica Sinica, 2018, 38(4): 0410004.