[1]刘哲,黄文准,乌伟.基于级联线性回归的快速单幅图像超分辨率技术[J].红外技术,2018,40(9):894-901.[doi:10.11846/j.issn.1001_8891.201809011]
 LIU Zhe,HUANG Wenzhun,WU Wei.Fast Single Image Super Resolution Based on Cascaded Linear Regression[J].Infrared Technology,2018,40(9):894-901.[doi:10.11846/j.issn.1001_8891.201809011]
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基于级联线性回归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9期
页码:
894-901
栏目:
出版日期:
2018-09-20

文章信息/Info

Title:
Fast Single Image Super Resolution Based on Cascaded Linear Regression
文章编号:
1001-8891(2018)09-0894-08
作者:
刘哲黄文准乌伟
西京学院 信息工程学院
Author(s):
LIU ZheHUANG WenzhunWU Wei
Department of Electronic and Information Engineering, Xijing University
关键词:
超分辨率样本学习级联线性回归最小二乘
Keywords:
super resolutionexemplars learningcascaded linear regressionleast squares
分类号:
TP391.41
DOI:
10.11846/j.issn.1001_8891.201809011
文献标志码:
A
摘要:
基于学习的图像超分辨率技术,通过学习获得高、低分辨率图像之间的映射关系,将其作为先验约束条件来估计高分辨率图像。这种技术的一个重要问题是如何建立高分辨率和低分辨率图像之间的映射关系,大多数现有的复杂模型既难以推广到所有自然图像,还需要耗费大量时间进行模型训练,而简单模型的表示能力却很有限。本文提出了一种简单、有效、鲁棒、快速的图像超分辨率技术。这种超分辨技术基于一系列线性最小二乘函数,即级联线性回归模型,这种模型函数具有闭合形式的解,仅需要很少的控制参数,因此在计算上能够有效实现。为了减小估计模型和实际模型之间的差距,本文通过k-means算法将图像块进行聚类,并在每次迭代中学习每个聚类的线性回归参数,在级联线性回归学习过程中逐渐逼近真实的超分辨率图像。实验结果表明,本文所提出的技术与现有技术方法相比,具有更好的超分辨性能、更低的时间消耗。
Abstract:
Example-learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high-and low-resolution image pairs. An important issue for these techniques is how to model the relationship between the high- and low-resolution image patches: most exist-ing complex models either generalize hard to diverse natural images or require a lot of time for model train-ing, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image super-resolver for image super-resolution. The proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. It has few parameters to control the model and is thus able to robustly adapt to different image datasets and experimental settings. The linear least square functions lead to closed-form solutions and therefore achieve computationally effi-cient implementations. To effectively decrease gaps, we group image patches into clusters using the k-means algorithm and learn a linear regress or for each cluster at each iteration. The cascaded learning process grad-ually decreases the gap of high-frequency detail between the estimated high-resolution image patch and the ground-truth image patch and simultaneously obtains the linear regression parameters. Experimental results show that the proposed method achieves superior performance with better efficiency than the existing state-of-the-art methods.

参考文献/References:

[1]? Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, et al. Accelerating su-per-resolution for 4 K upscaling[C]//IEEE Trans. Cons. Elec., 2015: 317-320.?
[2]? YANG J, Wright J, HUANG T S, et al. Image super -resolution via sparse representation[J]. IEEE Trans. Image Process., 2010, 19(11): 2861-2873.?
[3]? Freeman W T, Jones T R, Pasztor E C. Exaple-based Super-Resolution[J]. CGA, 2002, 22(2):56-65.?
[4]? Sakurai M, Sakuta Y, Watanabe M, et al. Super-resolution through non -linear enhancement filters[C]// IEEE Int. Conf. on Image Proc., 2013: 854-858.?
[5]? YANG C Y, HUANG J B, YANG M H. Exploiting self-similarities for single frame super -resolution[C]//Asian Conference on Computer Vision, 2010: 497-510.
[6]? ZHANG Y, LIU J, YANG W, et al. Image super -resolution based on structure-modulated sparse representation[J]. IEEE Transactions on Im-age Processing, 2015, 24(9): 2797-2810.
[7]? HUANG J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5197-5206.
[8]? Choi J S, Bae S H, Kim M. Single image super -resolution based on self-examples using context -dependent subpatches[C]//2015 IEEE In-ternational Conference on Image Processing(ICIP), 2015: 2835 -2839.
[9]? Ebrahimi M, Vrscay E. Solving the inverse problem of image zooming using self-examples[C]// International Conference on Image Analysis and Recognition, 2007: DOI: 10.1007/978-3-540-74260-9_11.
[10]? Buades A, Coll B, Morel J. A non-local algorithm for image denoising[C] //IEEE Conference on Computer Vision and Pattern Recognition, 2005.
[11]? Freedman G, Fattal R. Image and video upscaling from local self-examples[J]. ACM Trans. on Graphics, 2011, 30(2): 12.
[12]? 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.
[13]? Singh A, Porikli F, Ahuja N. Super-resolving noisy images[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2014: DOI: 10.1109/CVPR.2014.364.
[14]? Freeman W T, Jones T R, Pasztor E C. Example -based su-per-resolution[J]. IEEE Computer graphics and Applications, 2002, 22(2): 56-65.
[15]? Timofte R, De Smet V, Van Gool L. Anchored neighborhood regression for fast example-based super -resolution[C]//Proceedings of the IEEE International Conference on Computer Vision, 2013: 1920-1927.
[16]? 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.
[17]? Dong C, Loy C C, He K, et al. Image super -resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307.
[18]? YANG J, Wright J, Huang T S, et al. Image super -resolution via sparse representation[J]//IEEE Trans. Image Process., 2010, 19(11): 2861-2873.
[19]? Zeyde R, Elad M, Protter M. On single image scale-up using sparse -representations[C]//Proc. of 7th Int. Conf. Curves Surf., 2012: 711-730.
[20]? DONG C, LOY C C, HE K, et al. Learning a deep convolutional net-work for image super-resolution[C]// Proc. of Eur. Conf. Comput. Vis., 2014: 184-199.
[21]? DONG C, Loy C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38(2): 295-307.
[22]? Timofte R, Smet V De, Van Gool L. Anchored neighborhood regression for fast example-based super-resolution[C]//Proc. of IEEE Conf. Comput. Vis., 2013: 1920-1927.

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备注/Memo

备注/Memo:
收稿日期:2017-11-05;修订日期:2018-01-27.
作者简介:刘哲(1972-),男,教授级高工,博士,研究方向为机器视觉、人工智能及模式识别。E-mail:757417366@qq.com。
基金项目:国家自然科学基金(61473237)。

更新日期/Last Update: 2018-09-19