|本期目录/Table of Contents|

[1]周圆,王爱华,陈莹,等.基于非局部稀疏表示的立体图像的超分辨率重建[J].天津大学学报(自然科学版),2017,(04):377-384.[doi:10.11784/tdxbz201506020]
 Zhou Yuan,Wang Aihua,Chen Ying,et al.Stereo Image Super-Resolution Reconstruction Based on Non-Local Sparse Representation[J].Journal of Tianjin University,2017,(04):377-384.[doi:10.11784/tdxbz201506020]
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基于非局部稀疏表示的立体图像的超分辨率重建()
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《天津大学学报(自然科学版)》[ISSN:0493-2137/CN:12-1127/N]

卷:
期数:
2017年04
页码:
377-384
栏目:
电气自动化与信息工程
出版日期:
2017-04-30

文章信息/Info

Title:
Stereo Image Super-Resolution Reconstruction Based on Non-Local Sparse Representation
作者:
周圆 王爱华 陈莹 侯春萍
天津大学电气自动化与信息工程学院,天津 300072
Author(s):
Zhou Yuan Wang Aihua Chen Ying Hou Chunping
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
关键词:
超分辨率重建 稀疏表示 联合特征图像块 立体图像 联合字典学习
Keywords:
super-resolution reconstruction sparse representation joint characteristic image tiles stereo image joint dictionary learning
分类号:
TN911.73
DOI:
10.11784/tdxbz201506020
文献标志码:
A
摘要:
针对在立体图像的超分辨率重建过程中, 需要分别对低分辨率的彩图和同场景的深度图进行超分辨率重建的问题, 提出了一种基于联合稀疏表示的立体图像的超分辨率重建方法.该方法在非局部中心稀疏表示重建方法的基础上, 利用彩色图像与同场景深度图像的耦合相关性, 通过构造联合特征图像块来学习彩色和深度图像的联合字典; 然后构造彩色和深度图像块的联合编码增量作为正则项, 利用迭代优化算法求解模型, 进而同时重建高分辨率的彩色和深度图像.为验证算法的有效性, 在Middlebury数据集上对重建结果进行了主、客观评估, 并与不同算法进行了比较. 实验结果表明, 在客观指标和主观视觉效果上, 本文提出的算法可以同时获得令人满意的彩图和高质量的深度图.
Abstract:
To resolve the stereo image super-resolution reconstruction problem of reconstructing the color image and its corresponding depth map in the same scene respectively,a stereo image super-resolution reconstruction method based on the joint sparse representation is proposed in this paper. The model is developed based on the nonlocal center sparse representation method,using the correspondence between the color image and the depth map of the same scene. The joint dictionary is learned through joint characteristic image tiles construction. Then regularize the problem with joint coding increments of color and depth image tiles. Subsequently,an iterative optimization algorithm is applied to solve the proposed model. The high-resolution color image and its corresponding depth image are restored simultaneously. To evaluate the effectiveness of the proposed algorithm,several experiments on the Middlebury dataset are conducted,and the proposed algorithm and different methods in both objective indexes and subjective visual experience are compared. The experimental results show that the proposed algorithm achieves satisfactory super-resolution results in both objective indexes and subjective visual comparisons.

参考文献/References:

[1] Keys R G. Cubic convolution interpolation for digital im-age processing[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1981, 29(6):1153-1160.
[2] Hou H S, Andrews H. Cubic splines for image interpolation and digital filtering[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1978, 26(6):508-517.
[3] Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution [J]. Computer Graphics and Applications, 2002, 22(2):56-65.
[4] 杨爱萍, 钟腾飞, 何宇清, 等. 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.
Yang Aiping, Zhong Tengfei, He Yuqing, et al. Super-resolution reconstruction based on non-local similarity and clustered semi-coupled dictionary learning[J]. Journal of Tianjin University:Science and Technology, 2015, 48(1):87-94(in Chinese).
[5] 刘哲, 张永亮, 郝珉慧, 等. 一种快速超分辨率图像重建算法[J]. 光电子·激光, 2013, 24(2):372-377.
Liu Zhe, Zhang Yongliang, Hao Minhui, et al. A fast super resolution image reconstruction algorithm[J]. Journal of Optoelectronics·Laser, 2013, 24(2):372-377(in Chinese).
[6] Chang H, Yeung D, Xiong Y. Super-resolution through neighbor embedding [C]//IEEE Conference on Computer Vision & Patter Recognition. Washington, USA, 2004:275-282.
[7] Yang J, Wang Z, Lin Z, et al. Coupled dictionary training for image super-resolution[J]. IEEE Transactions on Image Processing, 2012, 21(8):3467-3478.
[8] Schulter S, Leistner C, Bischof H. Fast and accurate image upscaling with super-resolution forests[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015:3791-3799.
[9] Jing X Y, Zhu X, Wu F, et al. Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015:695-704.
[10] Zhu K, Lin F. Image super-resolution reconstruction by sparse decomposition and scale-invariant feature retrieval in micro-UAV stereo vision [C]//IEEE International Conference on Control and Automation. Taichung, Taiwan, China, 2014:705-710.
[11] Schuon S, Theobalt C, Davis J, et al. LidarBoost:Depth superresolution for ToF 3D shape scanning [C]//IEEE Conference on Computer Vision & Pattern Recognition. Miami, USA, 2009:343-350.
[12] Liu F, Shen C, Lin G. Deep convolutional neural fields for depth estimation from a single image[C]//IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009:343-350.
[13] Freedman G, Fattal R. Image and video upscaling from local self-examples[J]. ACM transactions on Graphics, 2011, 30(2):474-484.
[14] Li Y, Xue T, Sun L, et al. Joint example-based depth map super-resolution[C]//2012 IEEE International Conference on Multimedia and Expo. Melbourne, Australia, 2012:152-157.
[15] Zhuo W, Salzmann M, He X, et al. Indoor scene structure analysis for single image depth estimation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015:614-622.
[16] Lu J, Forsyth D. Sparse depth super resolution [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015:2245-2253.
[17] Kiechle M, Hawe S, Kleinsteuber M. A joint intensity and depth co-sparse analysis model for depth map super-resolution [C]//2013 IEEE International Conference on Computer Vision. Sydney, Australia, 2013:1545-1552.
[18] Glasner D, Bagon S, Irani M. Super-resolution from a single image[C]//2009 IEEE 12th International Confevence on Computer Vision. Kyoto, Japan, 2009:349-356.
[19] Dong W, Zhang L, Shi G, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization [J]. IEEE Transactions on Image Processing, 2011, 20(7):1838-1857.
[20] Buades A, Coll B, Morel J M. A review of image de
noising algorithms, with a new one[J]. Multiscale Modeling and Simulation, 2005, 4(2):490-530.
[21] Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering [J]. IEEE Transactions on Image Processing, 2007, 16(8):2080-2095.
[22] Dong W, Zhang L, Shi G, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2013, 22(4):1620-1630.
[23] Daubechies I, Defrise M, de Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint [J]. Communications on Pure and Applied Mathematics, 2003, 57(11):1413-1457.
[24] 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.
[25] Ferstl D, Reinbacher C, Ranftl R, et al. Image guided depth up-sampling using anisotropic total generalized variation [C]//2013 IEEE International Conference on Computer Vision. Sydney, Australia, 2013:993-1000.

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备注/Memo

备注/Memo:
收稿日期: 2015-06-07; 修回日期: 2016-08-29.
作者简介: 周圆(1982—), 女, 副教授.
通讯作者: 周圆, zhouyuan@tju.edu.cn.
基金项目: 国家自然科学基金资助项目(61201179, 61571326).
Supported by the National Natural Science Foundation of China(No. 61201179, 61571326).
更新日期/Last Update: 2017-04-10