[1]姜杰,刘哲,吕林涛.局部线性嵌入的快速单幅图像超分辨率技术[J].红外技术,2018,40(1):039-46.[doi:10.11846/j.issn.1001_8891.201801008]
 JIANG Jie,LIU Zhe,LV Lintao.Fast Single-image Super Resolution Technique Based on Local Linear Embedding[J].Infrared Technology,2018,40(1):039-46.[doi:10.11846/j.issn.1001_8891.201801008]
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局部线性嵌入的快速单幅图像超分辨率技术
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
40
期数:
2018年第1期
页码:
039-46
栏目:
出版日期:
2018-01-20

文章信息/Info

Title:
Fast Single-image Super Resolution Technique Based on Local Linear Embedding
文章编号:
1001-8891(2018)01-0039-08
作者:
姜杰刘哲吕林涛
西京学院 信息工程学院
Author(s):
JIANG JieLIU ZheLV Lintao
Department of electronic and information engineering, Xijing University
关键词:
超分辨率局部线性嵌入样本学习
Keywords:
super resolutionlocal linear embeddingexemplars learning
分类号:
TP391.41
DOI:
10.11846/j.issn.1001_8891.201801008
文献标志码:
A
摘要:
图像超分辨率的目的是在给定低分辨率图像的基础上产生超分辨率图像。单幅图像超分辨率是个病态和欠定的问题,需要通过样本学习和图像先验约束来重构图像丢失的高频细节。本文提出了一种基于局部线性嵌入的快速单幅图像超分辨率技术。首先,该方法利用大量的自然图像建立高低分辨率图像块样本训练库;其次,运用聚类算法将具有相似性质的高低分辨率样本块进行聚类;再次,基于局部线性嵌入技术,通过样本训练来学习低分辨率图像与高分辨率图像之间的映射函数;最后,用过映射函数来重构高分辨率图像。实验结果表明,本文算法不仅能高质量重构高分辨图像,而且快速高效。
Abstract:
The goal of single-image super-resolution is to generate a super-resolution image based on a given low-resolution input. It is an ill-posed and under-determined problem that requires exemplars or priors to re-construct the missing high-frequency details. In this paper, a fast single-image super-resolution technique based on local linear embedding is proposed. Firstly, this method uses a large number of natural images to establish a sample training library of high- and low-resolution image blocks. Secondly, the clustering algo-rithm is used to cluster the high- and low-resolution image blocks with similar properties. Thirdly, the map-ping function between the low- and high-resolution images is studied by sample training based on local li-near embedding techniques. Finally, the high-resolution image is reconstructed by using the mapping func-tion. The experimental results show that such an algorithm can reconstruct high-resolution high-quality im-ages fast and efficiently.

参考文献/References:

[1]? Lu X, Huang Z, Yuan Y. MR image super-resolution via manifold regularized sparse learning[J]. Neurocomputing, 2015, 162: 96-104.
[2]? Hu Y, Wnag N, Tao D, et al. SERF: A simple, effective, robust, and fast image super-resolver from cascaded linear regression[J]. IEEE Transactions on Image Processing, 2016, 25(9): 4091-4102.
[3]? Lu X, Yuan Y, Yan P. Alternatively constrained dictionary learning for image superresolution[J]. IEEE Transactions on Cybernetics, 2014, 44(3): 366-377.
[4]? Zhou Jinghong, Zhou Cui, Zhu Jianjun, et al. A method of su-per-resolution reconstruction for remote sensing image based on non-subsampled contourlet transform[J]. Acta Optica Sinica, 2015, 35(1): 0110001.?
[5]? Yang C Y, Yang M H. Fast Direct Super-Resolution by Simple Func-tions[C]//IEEE International Conference on Computer Vision, IEEE Computer Society, 2013: 561-568.
[6]? Pan Q, Liang Y, Zhang L, et al. Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthe-sis[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2012: 2216-2223.
[7]? Yang J, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2010, 19(11): 2861.
[8]? Roman Zeyde, Michael Elad, Matan Protter. On single image scale-up using sparse- representations[C]//International Conference on Curves and Surfaces, Springer-Verlag, 2010: 711-730.
[9]? Timofte R, De V, Gool L V. Anchored Neighborhood Regression for Fast Example-Based Super-Resolution[C]//IEEE International Conference on Computer Vision, IEEE, 2014: 1920-1927.
[10]? Yang J, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2010, 19(11): 2861-2873.
[11]? Roman Zeyde, Michael Elad, Matan Protter. On Single Image Scale-Up Using Sparse-Representations[C]//International Conference on Curves and Surfaces. Springer-Verlag, 2010: 711-730.
[12]? Timofte R, Smet V D, Gool L V. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution[M]//Computer Vision-ACCV 2014. Springer International Publishing, 2014: 111-126.
[13]? Yang C Y, Yang M H. Fast Direct Super-Resolution by Simple Func-tions[C]//IEEE International Conference on Computer Vision, IEEE Computer Society, 2013: 561-568.
[14]? Yang J, Lin Z, Cohen S. Fast Image Super-Resolution Based on In-Place Example Regression[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2013: 1059-1066.
[15]? Cui Z, Chang H, Shan S, et al. Deep Network Cascade for Image Super-resolution[M]//Computer Vision – ECCV 2014. Springer International Publishing, 2014: 49-64.
[16]? Timofte R, Smet V D, Gool L V. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution[C]//Asian Conference on Computer Vision. Springer, Cham, 2014: 111-126.
[17]? Kim K I, Kwon Y. Single-image super-resolution using sparse regression and natural image prior[J]. PAMI, 2010, 32(6): 1127–1133.
[18]? WANG S, ZHANG L, LIANG Y, et al. Semi-coupled dictionary learning with applications to image super-resolution and photosketch synthe-sis[C]//CVPR, 2012.
[19]? Sun J, Shum H Y. Image super-resolution using gradient profile prior: IEEE, US9064476[P]. 2015.
[20]? Nasrollahi K, Moeslund T B. Super-resolution: a comprehensive survey[J]. Machine Vision & Applications, 2014, 25(6):1423-1468.
[21]? Mairal J, Bach F, Ponce J, et al. Non-local sparse models for image restoration[C]//IEEE, International Conference on Computer Vision. IEEE, 2010: 2272-2279.
[22]? Glasner D, Bagon S, Irani M. Super-resolution from a single im-age[C]//IEEE, International Conference on Computer Vision. IEEE Xplore, 2009: 349-356.
[23]? Freedman G, Fattal R. Image and video upscaling from local self-examples[J]. ACM Transactions on Graphics, 2011, 30(2):1-11.
[24]? Kawano H, Suetake N, Cha B, et al. Sharpness preserving image enlargement by using self-decomposed codebook and Mahalanobis distance[J]. Image & Vision Computing, 2009, 27(6): 684-693.
[25]? Zhang Y Q, Liu J Y, Yang W H, et al. Image super-resolution based on structure-modulated sparse representation[J]. IEEE Transactions on Image Processing, 2015, 24(9): 2797-2810.?
[26]? Zhang Y Q, Xiao J S, Li S H, et al. Learning block-structured incoherent dictionaries for sparse representation[J]. Science China Information Sciences, 2015, 58(10): 1-15.
[27]? Yang C Y, Huang J B, Yang M H. Exploiting self-similarities for single frame super-resolution[C]// Asian Conference on Computer Vision. Springer-Verlag, 2010: 497-510.

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备注/Memo

备注/Memo:
收稿日期:2017-05-17;修订日期:2018-01-09.
作者简介:姜杰(1984-),女,讲师,硕士,研究方向为图像处理、传感器网络应用。
基金项目:国家自然科学基金项目(61473237);西京学院科研基金项目(XJ150121)。

更新日期/Last Update: 2018-01-18