[1]杨欣,姜斌,周大可.基于退化模型和邻域嵌套的彩色图像超分辨率自适应重建[J].东南大学学报(自然科学版),2011,41(6):1193-1196.[doi:10.3969/j.issn.1001-0505.2011.06.013]
 Yang Xin,Jiang Bin,Zhou Dake.Degradation model and neighbor embedding based color image adaptive super-resolution reconstruction[J].Journal of Southeast University (Natural Science Edition),2011,41(6):1193-1196.[doi:10.3969/j.issn.1001-0505.2011.06.013]
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基于退化模型和邻域嵌套的彩色图像超分辨率自适应重建()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第6期
页码:
1193-1196
栏目:
信息与通信工程
出版日期:
2011-11-20

文章信息/Info

Title:
Degradation model and neighbor embedding based color image adaptive super-resolution reconstruction
作者:
杨欣12姜斌1周大可1
(1 南京航空航天大学自动化学院,南京210016)(2 北京师范大学遥感科学国家重点实验室, 北京100875)
Author(s):
Yang Xin12Jiang Bin1Zhou Dake1
(1 College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
(2 National Laboratory on Machine Perception, Beijing Normal University, Beijing 100875, China)
关键词:
超分辨率重建 退化模型 邻域嵌套 特征提取
Keywords:
super-resolution reconstruction degradation model neighbor embedding feature extraction
分类号:
TN911
DOI:
10.3969/j.issn.1001-0505.2011.06.013
摘要:
为有效提升图像质量,提出一种基于图像退化模型和邻域嵌套的彩色图像超分辨率重建算法.通过退化模型在彩色空间上得出图像超分辨率重建训练集,并根据此训练集进行图像邻域分块.为了在训练过程中抑制噪声并锐化图像中的边缘信息,提取训练集亮度和梯度特征并进行特征融合.为了有效提升重建算法的自适应性,引入图像重建优化参数和边缘信息参数,根据此参数给出重建模型.基于最小二乘法的基本思想得出重建图像分块,并根据对应的次序对分块进行组合,得出最终高分辨率图像.实验结果表明,该算法效果良好.
Abstract:
A new color image super-resolution reconstruction method base on degradation model and neighbor embedding is proposed to effectively improve the image quality. Through image degradation model training set is obtained in color space and then according to this set the image area is cut into patches. A new combination of features is proposed based on luminance and gradient, which can preserve edges and smoothen color regions. Then image super-resolution optimization parameter (SOP) and edge information parameter (EIP) are introduced in order to effectively enhance the adaption of the algorithm and according to which, SR (super-resolution) model is given. Finally, the specific process of patches by Least Squares method is obtained. And the patches are stitched according to the corresponding coordinates, where the overlapping regions of patches are averaged. Experiments show that the proposed algorithm performs better in both quantitative and qualitative evaluation.

参考文献/References:

[1] Chaudhuri S. Super-resolution imaging [M]. Norwell, MA,USA: Kluwer, 2001.
[2] Tom B C, Katsaggelos A K. Reconstruction of a high-resolution image from multiple degraded mis-registered low-resolution images[J]. Visual Communications and Image Processing, 1994, 2308(9): 971-981.
[3] Hardie R C, Barnard K J, Armstrong E E. Joint MAP registration and high-resolution image estimation using a sequence of under sampled images [J]. IEEE Transaction on Image Processing, 1997, 6(9): 1621-1633.
[4] 杨欣, 王从庆, 费树岷. 基于最大后验概率的SAR图像自适应超分辨率盲重建[J]. 宇航学报, 2010, 31(1):217-221.
  Yang Xin, Wang Chongqing, Fei Shumin. An adaptive technology for SAR image blind super-resolution based on MAP [J]. Journal of Astronautics, 2010, 31(1):217-221. (in Chinese)
[5] Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution [J]. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
[6] Chen M, Qiu G, Lam K M. Example selective and order independent learning-based image super-resolution [C]//International Symposium on Intelligent Signal Processing and Communication Systems. Seoul, Korea, 2005: 77-80.
[7] Chang H, Yeung D Y, Xiong Y. Super-resolution through neighbor embedding [C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC, USA, 2004: 275-282.
[8] Wei F, Yeung D Y. Image hallucination using neighbor embedding over visual primitive manifolds [C]//IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA, 2007: 201-205.
[9] Chan Tak-Ming, Zhang Junping, Pu Jian, et al. Neighbor embedding based super-resolution algorithm through edge detection and feature selection [J]. Pattern Recognition Letters, 2009, 30(2): 494-502.

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备注/Memo

备注/Memo:
作者简介: 杨欣(1978—),男,博士,副教授,yangxin@nuaa.edu.cn.
基金项目: 国家自然科学基金资助项目(60905009)、高等学校博士学科点专项科研基金资助项目(20093218120015 )、中国科学院遥感应用研究所、北京师范大学遥感科学国家重点实验室开放基金资助项目(2009KFJJ012)、南京航空航天大学基本科研业务费专项科研资助项目(NS2010081).
引文格式: 杨欣,姜斌,周大可.基于退化模型和邻域嵌套的彩色图像超分辨率自适应重建[J].东南大学学报:自然科学版,2011,41(6):1193-1196. [doi:10.3969/j.issn.1001-0505.2011.06.013]
更新日期/Last Update: 2011-11-20